7,252 research outputs found
On Spectral Graph Embedding: A Non-Backtracking Perspective and Graph Approximation
Graph embedding has been proven to be efficient and effective in facilitating
graph analysis. In this paper, we present a novel spectral framework called
NOn-Backtracking Embedding (NOBE), which offers a new perspective that
organizes graph data at a deep level by tracking the flow traversing on the
edges with backtracking prohibited. Further, by analyzing the non-backtracking
process, a technique called graph approximation is devised, which provides a
channel to transform the spectral decomposition on an edge-to-edge matrix to
that on a node-to-node matrix. Theoretical guarantees are provided by bounding
the difference between the corresponding eigenvalues of the original graph and
its graph approximation. Extensive experiments conducted on various real-world
networks demonstrate the efficacy of our methods on both macroscopic and
microscopic levels, including clustering and structural hole spanner detection.Comment: SDM 2018 (Full version including all proofs
Top quark decays with flavor violation in the B-LSSM
The decays of top quark are extremely rare processes in the
standard model (SM). The predictions on the corresponding branching ratios in
the SM are too small to be detected in the future, hence any measurable signal
for the processes at the LHC is a smoking gun for new physics. In the extension
of minimal supersymmetric standard model with an additional local
gauge symmetry (B-LSSM), new gauge interaction and new flavor changing
interaction affect the theoretical evaluations on corresponding branching
ratios of those processes. In this work, we analyze those processes in the
B-LSSM, under a minimal flavor violating assumption for the soft breaking
terms. Considering the constraints from updated experimental data, the
numerical results imply ,
, and in our
chosen parameter space. Simultaneously, new gauge coupling constants
in the B-LSSM can also affect the numerical results of
.Comment: 20 pages, 4 figures, published in EPJC. arXiv admin note: substantial
text overlap with arXiv:1803.0990
A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network
This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability of recognizing fatigue driving. This method measures driving states using multifeature fusion. First, FNN is introduced to obtain the basic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function. Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists. Finally, the recognition result is given according to the combination of revised evidence based on Dempster’s rule. Experiment results demonstrate that the recognition method proposed in this paper can obtain reasonable results with the combination of information given by multiple features. The proposed method can also effectively and accurately describe driving states
Missed ferroelectricity in methylammonium lead iodide
Methylammonium lead iodide, as related organometal halide perovskites,
emerged recently as a particularly attractive material for photovoltaic
applications. The origin of its appealing properties is sometimes assigned to
its potential ferroelectric character, which remains however a topic of intense
debate. Here, we rationalize from first-principles calculations how the spatial
arrangement of methylammonium polar molecules is progressively constrained by
the subtle interplay between their tendency to bond with the inorganic
framework and the appearance of iodine octahedra rotations inherent to the
perovskite structure. The disordered tetragonal phase observed at room
temperature is paraelectric. We show that it should a priori become
ferroelectric but that iodine octahedra rotations drive the system toward an
antipolar orthorhombic ground state, making it a missed ferroelectric
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