Novel Compact Three-Way Filtering Power Divider Using Net-Type Resonators

In this paper, we present a novel compact three-way power divider with bandpass responses. The proposed power divider utilizes folded net-type resonators to realize dual functions of filtering and power splitting as well as compact size. Equal power ratio with low magnitude imbalance is achieved due to the highly symmetric structure. For demonstration, an experimental three way filtering power divider is implemented. Good filtering and power division characteristics are observed in the measured results of the circuit. The area of the circuits is 14.5 mm x 21.9 mm or 0.16 λg x 0.24 λg, where the λg is the guide wavelength of the center frequency at 2.1 GHz

Anisotropic distributions in a multi-phase transport model

With A Multi-Phase Transport (AMPT) model we investigate the relation between the magnitude, fluctuations and correlations of the initial state spatial anisotropy $\varepsilon_{n}$ and the final state anisotropic flow coefficients $v_{n}$ in Au+Au collisions at $\sqrt{s_{_{\rm NN}}}=$ 200 GeV. It is found that the relative eccentricity fluctuations in AMPT account for the observed elliptic flow fluctuations, in agreement with measurements of the STAR collaboration. In addition, the studies based on 2- and multi-particle correlations and event-by-event distributions of the anisotropies suggest that the Elliptic-Power function is a promising candidate of the underlying probability density function of the event-by-event distributions of $\varepsilon_{n}$ as well as $v_{n}$. Furthermore, the correlations between different order symmetry planes and harmonics in the initial coordinate space and final state momentum space are presented. Non-zero values of these correlations have been observed. The comparison between our calculations and data will, in the future, shed new insight into the nature of the fluctuations of the Quark-Gluon Plasma produced in heavy ion collisions.Comment: 10 pages, 8 figures, accepted by PR

On Functional Decomposition of Multivariate Polynomials with Differentiation and Homogenization

In this paper, we give a theoretical analysis for the algorithms to compute functional decomposition for multivariate polynomials based on differentiation and homogenization which are proposed by Ye, Dai, Lam (1999) and Faug$\mu$ere, Perret (2006, 2008, 2009). We show that a degree proper functional decomposition for a set of randomly decomposable quartic homogenous polynomials can be computed using the algorithm with high probability. This solves a conjecture proposed by Ye, Dai, and Lam (1999). We also propose a conjecture such that the decomposition for a set of polynomials can be computed from that of its homogenization with high probability. Finally, we prove that the right decomposition factors for a set of polynomials can be computed from its right decomposition factor space. Combining these results together, we prove that the algorithm can compute a degree proper decomposition for a set of randomly decomposable quartic polynomials with probability one when the base field is of characteristic zero, and with probability close to one when the base field is a finite field with sufficiently large number under the assumption that the conjeture is correct

Neighborhood Matching Network for Entity Alignment

Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge. NMN estimates the similarities between entities to capture both the topological structure and the neighborhood difference. It provides two innovative components for better learning representations for entity alignment. It first uses a novel graph sampling method to distill a discriminative neighborhood for each entity. It then adopts a cross-graph neighborhood matching module to jointly encode the neighborhood difference for a given entity pair. Such strategies allow NMN to effectively construct matching-oriented entity representations while ignoring noisy neighbors that have a negative impact on the alignment task. Extensive experiments performed on three entity alignment datasets show that NMN can well estimate the neighborhood similarity in more tough cases and significantly outperforms 12 previous state-of-the-art methods.Comment: 11 pages, accepted by ACL 202

A New Transgenic Line Reporting pStat3 Signaling in Glia

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140303/1/zeb.2014.1502.pd