232 research outputs found
Optimal Estimator Design and Properties Analysis for Interconnected Systems with Asymmetric Information Structure
This paper studies the optimal state estimation problem for interconnected
systems. Each subsystem can obtain its own measurement in real time, while, the
measurements transmitted between the subsystems suffer from random delay. The
optimal estimator is analytically designed for minimizing the conditional error
covariance. The boundedness of the expected error covariance (EEC) is analyzed.
In particular, a new condition that is easy to verify is established for the
boundedness of EEC. Further, the properties of EEC with respect to the delay
probability are studied. We found that there exists a critical probability such
that the EEC is bounded if the delay probability is below the critical
probability. Also, a lower and upper bound of the critical probability is
derived. Finally, the proposed results are applied to a power system, and the
effectiveness of the designed methods is illustrated by simulations
Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling
High-resolution (HR) images are usually downscaled to low-resolution (LR)
ones for better display and afterward upscaled back to the original size to
recover details. Recent work in image rescaling formulates downscaling and
upscaling as a unified task and learns a bijective mapping between HR and LR
via invertible networks. However, in real-world applications (e.g., social
media), most images are compressed for transmission. Lossy compression will
lead to irreversible information loss on LR images, hence damaging the inverse
upscaling procedure and degrading the reconstruction accuracy. In this paper,
we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware
image rescaling. To tackle the distribution shift, we first develop an
end-to-end asymmetric framework with two separate bijective mappings for
high-quality and compressed LR images, respectively. Then, based on empirical
analysis of this framework, we model the distribution of the lost information
(including downscaling and compression) using isotropic Gaussian mixtures and
propose the Enhanced Invertible Block to derive high-quality/compressed LR
images in one forward pass. Besides, we design a set of losses to regularize
the learned LR images and enhance the invertibility. Extensive experiments
demonstrate the consistent improvements of SAIN across various image rescaling
datasets in terms of both quantitative and qualitative evaluation under
standard image compression formats (i.e., JPEG and WebP).Comment: Accepted by AAAI 2023. Code is available at
https://github.com/yang-jin-hai/SAI
Origin of Hilbert space quantum scars in unconstrained models
Quantum many-body scar is a recently discovered phenomenon weakly violating
eigenstate thermalization hypothesis, and it has been extensively studied
across various models. However, experimental realizations are mainly based on
constrained models such as the model. Inspired by recent experimental
observations on the superconducting platform in Refs.~[Nat. Phys. 19, 120
(2022)] and [arXiv:2211.05803], we study a distinct class of quantum many-body
scars based on a half-filling hard-core Bose-Hubbard model, which is generic to
describe in many experimental platforms. It is the so-called Hilbert space
quantum scar as it originates from a subspace with a hypercube geometry weakly
connecting to other thermalization regions in Hilbert space. Within the
hypercube, a pair of collective Fock states do not directly connect to the
thermalization region, resulting in slow thermalization dynamics with
remarkable fidelity revivals with distinct differences from dynamics of other
initial states. This mechanism is generic in various real-space lattice
configurations, including one-dimensional Su-Schrieffer-Heeger chain, comb
lattice, and even random dimer clusters consisting of dimers. In addition, we
develop a toy model based on Hilbert hypercube decay approximation, to explain
the spectrum overlap between the collective states and all eigenstates.
Furthermore, we explore the Hilbert space quantum scar in two- and
three-dimensional Su-Schrieffer-Heeger many-body systems, consisting of
tetramers or octamers, respectively. This study makes quantum many-body scar
state more realistic in applications such as quantum sensing and quantum
metrology
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