9,458 research outputs found
Strong deflection gravitational lensing by a modified Hayward black hole
A modified Hayward black hole is a nonsingular black hole. It is proposed to
form when the pressure generated by quantum gravity can stop matter's collapse
as the matter reaches Planck density. Strong deflection gravitational lensing
happening nearby its event horizon might provide some clues of these quantum
effects in its central core. We investigate observables of the strong
deflection lensing, including angular separations, brightness differences and
time delays between its relativistic images, and estimate their values for the
supermassive black hole in the Galactic center. We find that it is possible to
distinguish the modified Hayward black hole from a Schwarzschild one, but it
demands very high resolution beyond current stage.Comment: 10 pages, 1 figur
Towards Structured Deep Neural Network for Automatic Speech Recognition
In this paper we propose the Structured Deep Neural Network (Structured DNN)
as a structured and deep learning algorithm, learning to find the best
structured object (such as a label sequence) given a structured input (such as
a vector sequence) by globally considering the mapping relationships between
the structure rather than item by item.
When automatic speech recognition is viewed as a special case of such a
structured learning problem, where we have the acoustic vector sequence as the
input and the phoneme label sequence as the output, it becomes possible to
comprehensively learned utterance by utterance as a whole, rather than frame by
frame.
Structured Support Vector Machine (structured SVM) was proposed to perform
ASR with structured learning previously, but limited by the linear nature of
SVM. Here we propose structured DNN to use nonlinear transformations in
multi-layers as a structured and deep learning algorithm. It was shown to beat
structured SVM in preliminary experiments on TIMIT
Improving information centrality of a node in complex networks by adding edges
The problem of increasing the centrality of a network node arises in many
practical applications. In this paper, we study the optimization problem of
maximizing the information centrality of a given node in a network
with nodes and edges, by creating new edges incident to . Since
is the reciprocal of the sum of resistance distance
between and all nodes, we alternatively consider the problem of minimizing
by adding new edges linked to . We show that the
objective function is monotone and supermodular. We provide a simple greedy
algorithm with an approximation factor and
running time. To speed up the computation, we also present an
algorithm to compute -approximate
resistance distance after iteratively adding edges, the
running time of which is for any
, where the notation suppresses the factors. We experimentally demonstrate the effectiveness and
efficiency of our proposed algorithms.Comment: 7 pages, 2 figures, ijcai-201
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