Realizing quantum controlled phase-flip gate through quantum dot in silicon slow-light photonic crystal waveguide

We propose a scheme to realize controlled phase gate between two single photons through a single quantum dot in slow-light silicon photonic crystal waveguide. Enhanced Purcell factor and beta factor lead to high gate fidelity over broadband frequencies compared to cavity-assisted system. The excellent physical integration of this silicon photonic crystal waveguide system provides tremendous potential for large-scale quantum information processing.Comment: 9 pages, 3 figure

Weighted AdaGrad with Unified Momentum

Integrating adaptive learning rate and momentum techniques into SGD leads to a large class of efficiently accelerated adaptive stochastic algorithms, such as Nadam, AccAdaGrad, \textit{etc}. In spite of their effectiveness in practice, there is still a large gap in their theories of convergences, especially in the difficult non-convex stochastic setting. To fill this gap, we propose \emph{weighted AdaGrad with unified momentum}, dubbed AdaUSM, which has the main characteristics that (1) it incorporates a unified momentum scheme which covers both the heavy ball momentum and the Nesterov accelerated gradient momentum; (2) it adopts a novel weighted adaptive learning rate that can unify the learning rates of AdaGrad, AccAdaGrad, Adam, and RMSProp. Moreover, when we take polynomially growing weights in AdaUSM, we obtain its $\mathcal{O}(\log(T)/\sqrt{T})$ convergence rate in the non-convex stochastic setting. We also show that the adaptive learning rates of Adam and RMSProp correspond to taking exponentially growing weights in AdaUSM, which thereby provides a new perspesctive for understanding Adam and RMSProp. Lastly, comparative experiments of AdaUSM against SGD with momentum, AdaGrad, AdaEMA, Adam, and AMSGrad on various deep learning models and datasets are also provided

Knowledge Collaboration Evolution Mechanisms of IT Service Outsourcing Enterprises: An Analysis Based on a Complex Network

The purpose of this study is to explore the self-organization evolution mechanisms of IT service outsourcing enterprise knowledge systems. First, this paper conducts a microanalysis of knowledge activities and discusses the macro characteristics of different evolution phases of enterprise knowledge collaboration networks. Second, it proposes the dominant factors of knowledge collaboration phases. Finally, it builds a network model of enterprise knowledge systems, calculates network structure parameters to reflect the phase changes in knowledge collaboration, and analyses the evolution path of knowledge systems driven by different dominant factors. This paper uses a knowledge network model based on self-organization theory to study structural problems of enterprise knowledge system evolution, and this model provides practical and theoretical value for solving the problem of knowledge collaboration among IT service outsourcing enterprises

Hierarchical Semantic Community Detection in Information Networks: A Complete Information Graph Approach

In order to detect the hierarchical semantic community which is helpful to discover the true organization of information network,we propose a complete information graph approach. In this method, we first use complete information graphs including semantic edges and link edges to represent information networks. Then we define semantic modularity as an objective function, a measure that can express not only the tightness of links, but also the consistency of content. Next, we improve Lovain\u27s algorithm and propose simLV algorithm to detect communities on the complete information graph. This recursive algorithm itself can discover semantic communities of different sizes in the process of execution. Experiment results show the hierarchical community detected by the simLV algorithm performs better than the Louvain in measuring the consistency of semantic content for our approach takes into account the content attributes of nodes, which are neglected by many other methods. It can detect more meaningful community structures with consistent content and tight structure in information networks such as social networks, citation networks, web networks, etc., which is helpful to the application of information dissemination analysis, topic detection, public opinion detection, etc

A Chinese Medicine Formula “Xian-Jia-Tang” for Treating Bladder Outlet Obstruction by Improving Urodynamics and Inhibiting Oxidative Stress through Potassium Channels

The aim of this study is to investigate efficacy of a traditional Chinese medicine formula (named Xian-Jia-Tang, XJT) on bladder outlet obstruction (BOO) in rats and explore its mechanisms. Total 80 BOO model rats were established and randomly divided into 4 groups: physiological saline, XJT, Cesium Chloride (CC), and XJT and CC groups. Meanwhile, 12 rats were used as normal control. Bladder weight and urodynamics were measured. Oxidative stress level and mRNA expressions of potassium channels gene were detected in detrusor. The mRNA and protein levels of hypoxia inducible factor-α (HIF-α) in detrusor were detected by RT-PCR and Western blot. BOO model rats showed significantly higher bladder weight and abnormal urodynamics. XJT significantly improved the abnormal urodynamics and inhibited the oxidative stress and changes of mRNA levels of potassium channels genes in detrusor of BOO model rats. Moreover, KATP and SK2/3 mRNA were overexpressed in BOO model rats treated by XJT. Besides, the significantly increased levels of HIF-α mRNA and protein were also inhibited by XJT. However, these inhibition effects of XJT were weakened by CC. XJT could effectively improve the urodynamics and inhibit the oxidative stress caused by hypoxia through suppressing the role of potassium channels in BOO model rats