2,073 research outputs found
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 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
, 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
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