193 research outputs found
Gaussian entanglement witness and refined Werner-Wolf criterion for continuous variables
We use matched quantum entanglement witnesses to study the separable criteria
of continuous variable states. The witness can be written as an identity
operator minus a Gaussian operator. The optimization of the witness then is
transformed to an eigenvalue problem of a Gaussian kernel integral equation. It
follows a separable criterion not only for symmetric Gaussian quantum states,
but also for non-Gaussian states prepared by photon adding to or/and
subtracting from symmetric Gaussian states. Based on Fock space numeric
calculation, we obtain an entanglement witness for more general two-mode
states. A necessary criterion of separability follows for two-mode states and
it is shown to be necessary and sufficient for a two mode squeezed thermal
state and the related two-mode non-Gaussian states. We also connect the witness
based criterion with Werner-Wolf criterion and refine the Werner-Wolf
criterion.Comment: 11pages, 2 figure
An Optimization Framework For Anomaly Detection Scores Refinement With Side Information
This paper considers an anomaly detection problem in which a detection
algorithm assigns anomaly scores to multi-dimensional data points, such as
cellular networks' Key Performance Indicators (KPIs). We propose an
optimization framework to refine these anomaly scores by leveraging side
information in the form of a causality graph between the various features of
the data points. The refinement block builds on causality theory and a proposed
notion of confidence scores. After motivating our framework, smoothness
properties are proved for the ensuing mathematical expressions. Next, equipped
with these results, a gradient descent algorithm is proposed, and a proof of
its convergence to a stationary point is provided. Our results hold (i) for any
causal anomaly detection algorithm and (ii) for any side information in the
form of a directed acyclic graph. Numerical results are provided to illustrate
the advantage of our proposed framework in dealing with False Positives (FPs)
and False Negatives (FNs). Additionally, the effect of the graph's structure on
the expected performance advantage and the various trade-offs that take place
are analyzed
Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion
A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties
A Comparative Study of Image Restoration Networks for General Backbone Network Design
Despite the significant progress made by deep models in various image
restoration tasks, existing image restoration networks still face challenges in
terms of task generality. An intuitive manifestation is that networks which
excel in certain tasks often fail to deliver satisfactory results in others. To
illustrate this point, we select five representative image restoration networks
and conduct a comparative study on five classic image restoration tasks. First,
we provide a detailed explanation of the characteristics of different image
restoration tasks and backbone networks. Following this, we present the
benchmark results and analyze the reasons behind the performance disparity of
different models across various tasks. Drawing from this comparative study, we
propose that a general image restoration backbone network needs to meet the
functional requirements of diverse tasks. Based on this principle, we design a
new general image restoration backbone network, X-Restormer. Extensive
experiments demonstrate that X-Restormer possesses good task generality and
achieves state-of-the-art performance across a variety of tasks
HAT: Hybrid Attention Transformer for Image Restoration
Transformer-based methods have shown impressive performance in image
restoration tasks, such as image super-resolution and denoising. However, we
find that these networks can only utilize a limited spatial range of input
information through attribution analysis. This implies that the potential of
Transformer is still not fully exploited in existing networks. In order to
activate more input pixels for better restoration, we propose a new Hybrid
Attention Transformer (HAT). It combines both channel attention and
window-based self-attention schemes, thus making use of their complementary
advantages. Moreover, to better aggregate the cross-window information, we
introduce an overlapping cross-attention module to enhance the interaction
between neighboring window features. In the training stage, we additionally
adopt a same-task pre-training strategy to further exploit the potential of the
model for further improvement. Extensive experiments have demonstrated the
effectiveness of the proposed modules. We further scale up the model to show
that the performance of the SR task can be greatly improved. Besides, we extend
HAT to more image restoration applications, including real-world image
super-resolution, Gaussian image denoising and image compression artifacts
reduction. Experiments on benchmark and real-world datasets demonstrate that
our HAT achieves state-of-the-art performance both quantitatively and
qualitatively. Codes and models are publicly available at
https://github.com/XPixelGroup/HAT.Comment: Extended version of HA
Symmetry breaking induced insulating electronic state in PbCu(PO)O
The recent experimental claim of room-temperature ambient-pressure
superconductivity in a Cu-doped lead-apatite (LK-99) has ignited substantial
research interest in both experimental and theoretical domains. Previous
density functional theory (DFT) calculations with the inclusion of an on-site
Hubbard interaction consistently predict the presence of flat bands
crossing the Fermi level. This is in contrast to DFT plus dynamical mean field
theory calculations, which reveal the Mott insulating behavior for the
stoichiometric PbCu(PO)O compound. Nevertheless, the existing
calculations are all based on the structure, which is argued to be not
the ground-state structure. Here, we revisit the electronic structure of
PbCu(PO)O with the energetically more favorable
structure, fully taking into account electronic symmetry breaking. We examine
all possible configurations for Cu substituting the Pb sites. Our results show
that the doped Cu atoms exhibit a preference for substituting the Pb2 sites
than the Pb1 sites. In both cases, the calculated substitutional formation
energies are large, indicating the difficulty in incorporating Cu at the Pb
sites. We find that most of structures with Cu at the Pb2 site tend to be
insulating, while the structures with both two Cu atoms at the Pb1 sites
(except one configuration) are predicted to be metallic by DFT+
calculations. However, when accounting for the electronic symmetry breaking,
some Cu-doped configurations previously predicted to be metallic (including the
structure studied in previous DFT+ calculations) become insulating. Our work
highlights the importance of symmetry breaking in obtaining correct electronic
state for PbCu(PO)O, thereby reconciling previous DFT+ and
DFT+DMFT calculations.Comment: 19 pages, 9 figures (including Supplementary Material
Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods
Motivation: Pentatricopeptide repeat (PPR) is a triangular pentapeptide repeat domain that plays a vital role in plant growth. In this study, we seek to identify PPR coding genes and proteins using a mixture of feature extraction methods. We use four single feature extraction methods focusing on the sequence, physical, and chemical properties as well as the amino acid composition, and mix the features. The Max-Relevant-Max-Distance (MRMD) technique is applied to reduce the feature dimension. Classification uses the random forest, J48, and naïve Bayes with 10-fold cross-validation.Results: Combining two of the feature extraction methods with the random forest classifier produces the highest area under the curve of 0.9848. Using MRMD to reduce the dimension improves this metric for J48 and naïve Bayes, but has little effect on the random forest results.Availability and Implementation: The webserver is available at: http://server.malab.cn/MixedPPR/index.jsp
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