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 $T^{1.5}$, 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 $H_0$ 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 $c<1$ at more than $4\sigma$ confidence level. The inclusion of spatial curvature enlarges the error bars and leads to $c<1$ only in about $2.5\sigma$ range; in contrast, the inclusion of massive neutrinos does not have significant influence on $c$. We also find that, for the holographic dark energy model with spatial curvature but without massive neutrinos, the $3\sigma$ error bars of the current fractional curvature density $\Omega_{k0}$ are still in order of $10^{-2}$; for the model with massive neutrinos but without spatial curvature, the $2\sigma$ upper bound of the total mass of neutrinos is $\sum m_{\nu} < 0.48$ eV. Moreover, there exists clear degeneracy between spatial curvature and massive neutrinos in the holographic dark energy model, which enlarges the upper bound of $\sum m_{\nu}$ 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$\sigma$ 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