Unusual behavior of sound velocity of a Bose gas in an optical superlattice at quasi-one-dimension

A Bose gas trapped in a one-dimensional optical superlattice has emerged as a novel superfluid characterized by tunable lattice topologies and tailored band structures. In this work, we focus on the propagation of sound in such a novel system and have found new features on sound velocity, which arises from the interplay between the two lattices with different periodicity and is not present in the case of a condensate in a monochromatic optical lattice. Particularly, this is the first time that the sound velocity is found to first increase and then decrease as the superlattice strength increases even at one dimension. Such unusual behavior can be analytically understood in terms of the competition between the decreasing compressibility and the increasing effective mass due to the increasing superlattice strength. This result suggests a new route to engineer the sound velocity by manipulating the superlattice's parameters. All the calculations based on the mean-field theory are justified by checking the exponent $\gamma$ of the off-diagonal one-body density matrix that is much smaller than 1. Finally, the conditions for possible experimental realization of our scenario are also discussed.Comment: 10 pages, 5 figure

Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion

Point clouds scanned by real-world sensors are always incomplete, irregular, and noisy, making the point cloud completion task become increasingly more important. Though many point cloud completion methods have been proposed, most of them require a large number of paired complete-incomplete point clouds for training, which is labor exhausted. In contrast, this paper proposes a novel Reconstruction-Aware Prior Distillation semi-supervised point cloud completion method named RaPD, which takes advantage of a two-stage training scheme to reduce the dependence on a large-scale paired dataset. In training stage 1, the so-called deep semantic prior is learned from both unpaired complete and unpaired incomplete point clouds using a reconstruction-aware pretraining process. While in training stage 2, we introduce a semi-supervised prior distillation process, where an encoder-decoder-based completion network is trained by distilling the prior into the network utilizing only a small number of paired training samples. A self-supervised completion module is further introduced, excavating the value of a large number of unpaired incomplete point clouds, leading to an increase in the network's performance. Extensive experiments on several widely used datasets demonstrate that RaPD, the first semi-supervised point cloud completion method, achieves superior performance to previous methods on both homologous and heterologous scenarios

Gap Solitons and Bloch Waves in Nonlinear Periodic Systems

We comprehensively investigate gap solitons and Bloch waves in one-dimensional nonlinear periodic systems. Our results show that there exists a composition relation between them: Bloch waves at either the center or edge of the Brillouin zone are infinite chains composed of fundamental gap solitons(FGSs). We argue that such a relation is related to the exact relation between nonlinear Bloch waves and nonlinear Wannier functions. With this composition relation, many conclusions can be drawn for gap solitons without any computation. For example, for the defocusing nonlinearity, there are $n$ families of FGS in the $n$th linear Bloch band gap; for the focusing case, there are infinite number of families of FGSs in the semi-infinite gap and other gaps. In addition, the stability of gap solitons is analyzed. In literature there are numerical results showing that some FGSs have cutoffs on propagation constant (or chemical potential), i.e. these FGSs do not exist for all values of propagation constant (or chemical potential) in the linear band gap. We offer an explanation for this cutoff.Comment: A longer version of our recent paper, PRL 102, 093905 (2009

Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance.Comment: 19 pages, 7 figures, 4 table

Suppressing Ion Migration Enables Stable Perovskite Light-Emitting Diodes with All-Inorganic Strategy

Stability issue is one of the major concerns that limit emergent perovskite light-emitting diodes (PeLEDs) techniques. Generally, ion migration is considered as the most important origin of PeLEDs degradation. In this work, an all-inorganic device architecture, LiF/perovskite/LiF/ZnS/ZnSe, is proposed to address this imperative problem. The inorganic (Cs1-xRbx)(1-)(y)K(y)PbBr(3)perovskite is optimized with achieving a photoluminescence quantum yield of 67%. Depth profile analysis of X-ray photoelectron spectroscopy indicates that the LiF/perovskite/LiF structure and the ZnS/ZnSe cascade electron transport layers significantly suppress the electric-field-induced ion migrations of the perovskite layers, and impede the diffusion of metallic atoms from cathode into perovskites. The as-prepared PeLEDs display excellent shelf stability (maintaining 90% of the initial external quantum efficiency [EQE] after 264 h) and operational stability (half-lifetime of about 255 h at an initial luminance of 120 cd m(-2)). The devices also exhibit a maximum brightness of 15 6155 cd m(-2)and an EQE of 11.05%