1,864 research outputs found
Equation of motion for multiqubit entanglement in multiple independent noisy channels
We investigate the possibility and conditions to factorize the entanglement
evolution of a multiqubit system passing through multi-sided noisy channels. By
means of a lower bound of concurrence (LBC) as entanglement measure, we derive
an explicit formula of LBC evolution of the N-qubit generalized
Greenberger-Horne-Zeilinger (GGHZ) state under some typical noisy channels,
based on which two kinds of factorizing conditions for the LBC evolution are
presented. In this case, the time-dependent LBC can be determined by a product
of initial LBC of the system and the LBC evolution of a maximally entangled
GGHZ state under the same multi-sided noisy channels. We analyze the realistic
situations where these two kinds of factorizing conditions can be satisfied. In
addition, we also discuss the dependence of entanglement robustness on the
number of the qubits and that of the noisy channels.Comment: 14 page
A Fair and Secure Cluster Formation Process for Ad Hoc Networks
An efficient approach for organizing large ad hoc networks is to divide the nodes
into multiple clusters and designate, for each cluster, a clusterhead which is responsible for
holding intercluster control information. The role of a clusterhead entails rights and duties.
On the one hand, it has a dominant position in front of the others because it manages the
connectivity and has access to other node¿s sensitive information. But on the other hand, the
clusterhead role also has some associated costs. Hence, in order to prevent malicious nodes
from taking control of the group in a fraudulent way and avoid selfish attacks from suitable
nodes, the clusterhead needs to be elected in a secure way. In this paper we present a novel
solution that guarantees the clusterhead is elected in a cheat-proof manner
Dimensional crossover of thermal conductance in graphene nanoribbons: A first-principles approach
First-principles density-functional calculations are performed to investigate
the thermal transport properties in graphene nanoribbons (GNRs). The
dimensional crossover of thermal conductance from one to two dimensions (2D) is
clearly demonstrated with increasing ribbon width. The thermal conductance of
GNRs in a few nanometer width already exhibits an approximate low-temperature
dependence of , like that of 2D graphene sheet which is attributed to
the quadratic nature of dispersion relation for the out-of-plane acoustic
phonon modes. Using a zone-folding method, we heuristically derive the
dimensional crossover of thermal conductance with the increase of ribbon width.
Combining our calculations with the experimental phonon mean-free path, some
typical values of thermal conductivity at room temperature are estimated for
GNRs and for 2D graphene sheet, respectively. Our findings clarify the issue of
low-temperature dependence of thermal transport in GNRs and suggest a
calibration range of thermal conductivity for experimental measurements in
graphene-based materials.Comment: 18 pages, 4 figure
Atomic entanglement sudden death in a strongly driven cavity QED system
We study the entanglement dynamics of strongly driven atoms off-resonantly
coupled with cavity fields. We consider conditions characterized not only by
the atom-field coupling but also by the atom-field detuning. By studying two
different models within the framework of cavity QED, we show that the so-called
atomic entanglement sudden death (ESD) always occurs if the atom-field coupling
lager than the atom-field detuning, and is independent of the type of initial
atomic state
Association of LEP G2548A and LEPR Q223R Polymorphisms with Cancer Susceptibility: Evidence from a Meta-Analysis
__Background:__ Numerous epidemiological studies have examined associations of genetic variations in LEP (G2548A, -2548 nucleotide upstream of the ATG start site) and LEPR (Q223R, nonsynonymous SNP in exon 6) with cancer susceptibility; however, the findings are inconsistent. Therefore, we performed a meta-analysis to comprehensively evaluate such associations.
__Methods:__ We searched published literature from MEDLINE, EMBASE, Web of Science and CBM for eligible publications. We also assessed genotype-based mRNA expression data from HapMap for rs7799039 (G2548A) and rs1137101 (Q223R) in normal cell lines derived from 270 subjects with different ethnicities.
__Results:__ The final analysis included 16 published studies of 6569 cases and 8405 controls for the LEP G2548A and 19 studies of 7504 cases and 9581 controls for the LEPR Q223R. Overall, LEP G2548A was statistically significantly associated with an increased risk of overall cancer (AA vs. GG: OR=1.27, 95% CI=1.05-1.54; recessive model: OR=1.19, 9
Orthogonal methods based ant colony search for solving continuous optimization problems
Research into ant colony algorithms for solving continuous optimization problems forms one of the most
significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial
optimization, they have shown great potential in solving a wide range of optimization problems, including continuous
optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for
solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore
their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed
algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is
compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions
in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others
Holographic dark energy in a universe with spatial curvature and massive neutrinos: a full Markov Chain Monte Carlo exploration
In this paper, we report the results of constraining the holographic dark
energy model with spatial curvature and massive neutrinos, based on a Markov
Chain Monte Carlo global fit technique. The cosmic observational data include
the full WMAP 7-yr temperature and polarization data, the type Ia supernova
data from Union2.1 sample, the baryon acoustic oscillation data from SDSS DR7
and WiggleZ Dark Energy Survey, and the latest measurements of from HST.
To deal with the perturbations of dark energy, we adopt the parameterized
post-Friedmann method. We find that, for the simplest holographic dark energy
model without spatial curvature and massive neutrinos, the phenomenological
parameter at more than confidence level. The inclusion of
spatial curvature enlarges the error bars and leads to only in about
range; in contrast, the inclusion of massive neutrinos does not
have significant influence on . We also find that, for the holographic dark
energy model with spatial curvature but without massive neutrinos, the
error bars of the current fractional curvature density
are still in order of ; for the model with massive neutrinos but
without spatial curvature, the upper bound of the total mass of
neutrinos is eV. Moreover, there exists clear degeneracy
between spatial curvature and massive neutrinos in the holographic dark energy
model, which enlarges the upper bound of by more than 2 times.
In addition, we demonstrate that, making use of the full WMAP data can give
better constraints on the holographic dark energy model, compared with the case
using the WMAP ``distance priors''.Comment: 21 pages, 10 figures; major revision; new figures and discussions
added; accepted by JCA
A Catalog of Luminous Infrared Galaxies in the IRAS Survey and the Second Data Release of the SDSS
We select the Luminous Infrared Galaxies by cross-correlating the Faint
Source Catalogue (FSC) and Point Source Catalogue (PSC) of the IRAS Survey with
the Second Data Release of the SDSS for studying their infrared and optical
properties. The total number of our sample is 1267 for FSC and 427 for PSC by
using 2 significance level cross-section. The "likelihood ratio" method
is used to estimate the sample's reliability and for a more reliable subsample
(908 for FSC and 356 for PSC) selection. Then a Catalog with both the infrared,
optical and radio informations is presented and will be used in further works.
Some statistical results show that the Luminous Infrared Galaxies are quite
different from the Ultra-Luminous Infrared Galaxies. The AGN fractions of
galaxies with different infrared luminosities and the radio to infrared
correlations are consist with previous studies.Comment: 15 pages, 11 figures. Accepted by ChJAA. Reference adde
Accurate Diagnosis of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence
Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses.
Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, \u3e 14,680 WSIs, from \u3e 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany.
Results: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells.
Conclusions: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition
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