The influence of a single defect in composite gate insulators on the performance of nanotube transistors

The current through a carbon nanotube field-effect transistor (CNFET) with cylindrical gate electrode is calculated using the nonequilibrium Greens function method in a tight-binding approximation. The obtained result is in good agreement with the experimental data. The space radiation and nuclear radiation are known to cause defects in solids. The theoretical approach is used to calculate the amplitude of the random-telegraph-signal (RTS) noise due to a single defect in the gate oxide of a long channel p-type CNFET. We investigate how the amplitude of the RTS noise is affected by the composite structure of gate insulators, which contains an inner insulator with a dielectric constant larger than 3.9 and an outer insulator with a dielectric constant of 3.9 (as for SiO2). It is found that the RTS amplitude increases apparently with the decreasing thickness of the inner gate insulator. If the inner insulator is too thin, even though its dielectric constant is as large as 80, the amplitude of the RTS noise caused by the charge of Q = +1e may amount to around 80% in the turn-on region. Due to strong effects of defects in CNFETs, CNFETs have a potential to be used for detecting the space radiation or nuclear radiation.Comment: 8 Figure

Intrinsic Regularization Method in QCD

There exist certain intrinsic relations between the ultraviolet divergent graphs and the convergent ones at the same loop order in renormalizable quantum field theories. Whereupon we may establish a new method, the intrinsic regularization method, to regularize those divergent graphs. In this paper, we apply this method to QCD at the one loop order. It turns out to be satisfactory:The gauge invariance is preserved manifestly and the results are the same as those derived by means of other regularization methods.Comment: 18 pages, LaTeX , 7 figures in a separate compressed postscript fil

A Robust Quantum Random Access Memory

A "bucket brigade" architecture for a quantum random memory of $N=2^n$ memory cells needs $n(n+5)/2$ times of quantum manipulation on control circuit nodes per memory call. Here we propose a scheme, in which only average $n/2$ times manipulation is required to accomplish a memory call. This scheme may significantly decrease the time spent on a memory call and the average overall error rate per memory call. A physical implementation scheme for storing an arbitrary state in a selected memory cell followed by reading it out is discussed.Comment: 5 pages, 3 figure

Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although {\em social matrix factorization} (Social MF) and {\em topic matrix factorization} (Topic MF) successfully exploit social relations and item reviews, respectively, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model {\em \mbox{MR3}} to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.Comment: 27 pages, 11 figures, 6 tables, ACM TKDD 201

The SNR Puppis A Revisited with Seven Years of Fermi Large Area Telescope Observations

Puppis A is a very famous and extensively studied supernova remnant (SNR) that shows strong evidence of shock-cloud interaction. We re-analyze the GeV $\gamma$-ray emission of it using seven years Pass 8 data recorded by the Fermi Large Area Telescope (Fermi-LAT). The morphology of the $\gamma$-ray emission is more compatible with that of the thermal X-ray and IR emissions than the radio image, which suggests a possible correlation between the gamma-ray emitting region and dense clouds. The $\gamma$-ray spectrum in the energy range of 1-500 GeV shows a break at $7.92\pm1.91$ GeV with the photon indices of $1.81\pm0.08$ below the break and $2.53\pm0.12$ above the break, which can naturally explain the lack of TeV $\gamma$-ray emission from Puppis A. The multi-wavelength observations favor a hadronic origin for the $\gamma$-ray emission.Comment: 9 pages, 4 figures, 4 tables, Accepted for publication in Ap