Quantum Transports in Two-Dimensions with Long Range Hopping: Shielding, Localization and the Extended Isolated State

We investigate the effects of disorder and shielding on quantum transports in a two dimensional system with all-to-all long range hopping. In the weak disorder, cooperative shielding manifests itself as perfect conducting channels identical to those of the short range model, as if the long range hopping does not exist. With increasing disorder, the average and fluctuation of conductance are larger than those in the short range model, since the shielding is effectively broken and therefore long range hopping starts to take effect. Over several orders of disorder strength (until $\sim 10^4$ times of nearest hopping), although the wavefunctions are not fully extended, they are also robustly prevented from being completely localized into a single site. Each wavefunction has several localization centers around the whole sample, thus leading to a fractal dimension remarkably smaller than 2 and also remarkably larger than 0, exhibiting a hybrid feature of localization and delocalization. The size scaling shows that for sufficiently large size and disorder strength, the conductance tends to saturate to a fixed value with the scaling function $\beta\sim 0$, which is also a marginal phase between the typical metal ($\beta>0$) and insulating phase ($\beta<0$). The all-to-all coupling expels one isolated but extended state far out of the band, whose transport is extremely robust against disorder due to absence of backscattering. The bond current picture of this isolated state shows a quantum version of short circuit through long hopping.Comment: 15 pages, 8 figure

Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation

Audio-visual navigation is an audio-targeted wayfinding task where a robot agent is entailed to travel a never-before-seen 3D environment towards the sounding source. In this article, we present ORAN, an omnidirectional audio-visual navigator based on cross-task navigation skill transfer. In particular, ORAN sharpens its two basic abilities for a such challenging task, namely wayfinding and audio-visual information gathering. First, ORAN is trained with a confidence-aware cross-task policy distillation (CCPD) strategy. CCPD transfers the fundamental, point-to-point wayfinding skill that is well trained on the large-scale PointGoal task to ORAN, so as to help ORAN to better master audio-visual navigation with far fewer training samples. To improve the efficiency of knowledge transfer and address the domain gap, CCPD is made to be adaptive to the decision confidence of the teacher policy. Second, ORAN is equipped with an omnidirectional information gathering (OIG) mechanism, i.e., gleaning visual-acoustic observations from different directions before decision-making. As a result, ORAN yields more robust navigation behaviour. Taking CCPD and OIG together, ORAN significantly outperforms previous competitors. After the model ensemble, we got 1st in Soundspaces Challenge 2022, improving SPL and SR by 53% and 35% relatively.Comment: ICCV 202

Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing

To address the challenging task of instance-aware human part parsing, a new bottom-up regime is proposed to learn category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information over different human granularities and eases the difficulty of person partitioning. Specifically, a dense-to-sparse projection field, which allows explicitly associating dense human semantics with sparse keypoints, is learnt and progressively improved over the network feature pyramid for robustness. Then, the difficult pixel grouping problem is cast as an easier, multi-person joint assembling task. By formulating joint association as maximum-weight bipartite matching, a differentiable solution is developed to exploit projected gradient descent and Dykstra's cyclic projection algorithm. This makes our method end-to-end trainable and allows back-propagating the grouping error to directly supervise multi-granularity human representation learning. This is distinguished from current bottom-up human parsers or pose estimators which require sophisticated post-processing or heuristic greedy algorithms. Experiments on three instance-aware human parsing datasets show that our model outperforms other bottom-up alternatives with much more efficient inference.Comment: CVPR 2021 (Oral). Code: https://github.com/tfzhou/MG-HumanParsin

Orbital Magnetization under an Electric Field and Orbital Magnetoelectric Polarizabilty for a Bilayer Chern System

In the the real space formalism of orbital magnetization (OM) for a Chern insulator without an external electric field, it is correct to average the local OM either over the bulk region or over the whole sample. However for a layered Chern insulator in an external electric field, which is directly related to the nontrivial nature of orbital magnetoelectric coupling, the role of boundaries remains ambiguous in this formalism. Based on a bilayer model with an adjustable Chern number at half filling, we numerically investigate the OM with the above two different average types under a nonzero perpendicular electric field. The result shows that in this case, the nonzero Chern number gives rise to a gauge shift of the OM with the bulk region average, while this gauge shift is absent for the OM with the whole sample average. This indicates that only the whole sample average is reliable to calculate the OM under a nonzero electric field for Chern insulators. On this basis, the orbital magnetoelectric polarizablity (OMP) and the Chern-Simons orbital magnetoelectric polarizablity (CSOMP) with the whole sample average are studied. We also present the relationship between the OMP (CSOMP) and the response of Berry curvature to the electric field. The stronger the response of Berry curvature to electric field, the stronger is the OMP (CSOMP). Besides clarify the calculation methods, our result also provides an effective method to enhance OMP and CSOMP of materials.Comment: 11 pages, 11 figure