Quantum Hall Effects in a Non-Abelian Honeycomb Lattice

We study the tunable quantum Hall effects in a non-Abelian honeycomb optical lattice which is a many-Dirac-points system. We find that the quantum Hall effects present different features as change as relative strengths of several perturbations. Namely, a gauge-field-dressed next-nearest-neighbor hopping can induce the quantum spin Hall effect and a Zeeman field can induce a so-called quantum anomalous valley Hall effect which includes two copies of quantum Hall states with opposite Chern numbers and counter-propagating edge states. Our study extends the borders of the field of quantum Hall effects in honeycomb optical lattice when the internal valley degrees of freedom enlarge.Comment: 7 pages, 6 figure

Detecting a set of entanglement measures in an unknown tripartite quantum state by local operations and classical communication

We propose a more general method for detecting a set of entanglement measures, i.e. negativities, in an \emph{arbitrary} tripartite quantum state by local operations and classical communication. To accomplish the detection task using this method, three observers, Alice, Bob and Charlie, do not need to perform the partial transposition maps by the structural physical approximation; instead, they are only required to collectively measure some functions via three local networks supplemented by a classical communication. With these functions, they are able to determine the set of negativities related to the tripartite quantum state.Comment: 16 pages, 2 figures, revte

2,2′-[1-(2,4,6-Trichlorophenyl)-1H-1,2,4-triazole-3,5-diyl]diphenol

The title compound, C20H12Cl3N3O2, was synthesized by the reaction of 2-(2-hydroxy­phen­yl)benz[e][1,3]oxazin-4-one with 2,4,6-trichloro­phenyl­hydrazine in ethanol. The trichloro­phenyl ring is nearly perpendicular to the triazole plane [dihedral angle 80.56 (8)°], whereas the two hydroxy­phenyl rings are approximately coplanar with the triazole ring [dihedral angles of 2.79 (12) and 8.00 (14)°]. Intra­molecular O—H⋯N hydrogen bonding is observed between the hydroxy­phenyl and triazole rings

Photoluminescence pressure coefficients of InAs/GaAs quantum dots

We have investigated the band-gap pressure coefficients of self-assembled InAs/GaAs quantum dots by calculating 17 systems with different quantum dot shape, size, and alloying profile using atomistic empirical pseudopotential method within the ``strained linear combination of bulk bands'' approach. Our results confirm the experimentally observed significant reductions of the band gap pressure coefficients from the bulk values. We show that the nonlinear pressure coefficients of the bulk InAs and GaAs are responsible for these reductions. We also find a rough universal pressure coefficient versus band gap relationship which agrees quantitatively with the experimental results. We find linear relationships between the percentage of electron wavefunction on the GaAs and the quantum dot band gaps and pressure coefficients. These linear relationships can be used to get the information of the electron wavefunctions.Comment: 8 pages, 2 tables, 4 figure

Noiseless method for checking the Peres separability criterion by local operations and classical communication

We present a method for checking Peres separability criterion in an arbitrary bipartite quantum state $\rho_{AB}$ within local operations and classical communication scenario. The method does not require the prior state reconstruction and the structural physical approximation. The main task for the two observers, Alice and Bob, is to estimate some specific functions. After getting these functions, they can determine the minimal eigenvalue of $\rho^{T_{B}}_{AB}$, which serves as an entanglement indicator in lower dimensions.Comment: 10 pages, 2 figure

Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection

Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets, based on which researchers and developers can easily evaluate and improve their learning methods. Since the data collection is usually time-consuming or even expensive, how to protect their copyrights is of great significance and worth further exploration. In this paper, we revisit dataset ownership verification. We find that existing verification methods introduced new security risks in DNNs trained on the protected dataset, due to the targeted nature of poison-only backdoor watermarks. To alleviate this problem, in this work, we explore the untargeted backdoor watermarking scheme, where the abnormal model behaviors are not deterministic. Specifically, we introduce two dispersibilities and prove their correlation, based on which we design the untargeted backdoor watermark under both poisoned-label and clean-label settings. We also discuss how to use the proposed untargeted backdoor watermark for dataset ownership verification. Experiments on benchmark datasets verify the effectiveness of our methods and their resistance to existing backdoor defenses. Our codes are available at \url{https://github.com/THUYimingLi/Untargeted_Backdoor_Watermark}.Comment: This work is accepted by the NeurIPS 2022 (Oral, TOP 2%). The first two authors contributed equally to this work. 25 pages. We have fixed some typos in the previous versio

Improving Vision Transformers by Revisiting High-frequency Components

The transformer models have shown promising effectiveness in dealing with various vision tasks. However, compared with training Convolutional Neural Network (CNN) models, training Vision Transformer (ViT) models is more difficult and relies on the large-scale training set. To explain this observation we make a hypothesis that ViT models are less effective in capturing the high-frequency components of images than CNN models, and verify it by a frequency analysis. Inspired by this finding, we first investigate the effects of existing techniques for improving ViT models from a new frequency perspective, and find that the success of some techniques (e.g., RandAugment) can be attributed to the better usage of the high-frequency components. Then, to compensate for this insufficient ability of ViT models, we propose HAT, which directly augments high-frequency components of images via adversarial training. We show that HAT can consistently boost the performance of various ViT models (e.g., +1.2% for ViT-B, +0.5% for Swin-B), and especially enhance the advanced model VOLO-D5 to 87.3% that only uses ImageNet-1K data, and the superiority can also be maintained on out-of-distribution data and transferred to downstream tasks.Comment: 18 pages, 7 figure