38,794 research outputs found
Learning Deep Generative Models with Doubly Stochastic MCMC
We present doubly stochastic gradient MCMC, a simple and generic method for
(approximate) Bayesian inference of deep generative models (DGMs) in a
collapsed continuous parameter space. At each MCMC sampling step, the algorithm
randomly draws a mini-batch of data samples to estimate the gradient of
log-posterior and further estimates the intractable expectation over hidden
variables via a neural adaptive importance sampler, where the proposal
distribution is parameterized by a deep neural network and learnt jointly. We
demonstrate the effectiveness on learning various DGMs in a wide range of
tasks, including density estimation, data generation and missing data
imputation. Our method outperforms many state-of-the-art competitors
QCD Factorization For B Decays To Two Light Pseudoscalars Including Chirally Enhanced Corrections
Since b quark mass is not asymptotically large, chirally enhanced corrections
which arise from twist-3 wave functions may be important in B decays. We thus
evaluate the hadronic matrix elements with the emitted meson described by
leading twist and twist-3 distribution amplitudes . After summing
over the four "vertex correction" diagrams, we obtain the results with infrared
finiteness which shows that chirally enhanced corrections arise from
can be consistently included in QCD factorization. We also briefly
discuss the contributions from "hard spectator" diagrams.Comment: A revised versio
Phonon effect on two coupled quantum dots at finite temperature
The quantum oscillations of population in an asymmetric double quantum dots
system coupled to a phonon bath are investigated theoretically. It is shown how
the environmental temperature has effect on the system
Max-Mahalanobis Linear Discriminant Analysis Networks
A deep neural network (DNN) consists of a nonlinear transformation from an
input to a feature representation, followed by a common softmax linear
classifier. Though many efforts have been devoted to designing a proper
architecture for nonlinear transformation, little investigation has been done
on the classifier part. In this paper, we show that a properly designed
classifier can improve robustness to adversarial attacks and lead to better
prediction results. Specifically, we define a Max-Mahalanobis distribution
(MMD) and theoretically show that if the input distributes as a MMD, the linear
discriminant analysis (LDA) classifier will have the best robustness to
adversarial examples. We further propose a novel Max-Mahalanobis linear
discriminant analysis (MM-LDA) network, which explicitly maps a complicated
data distribution in the input space to a MMD in the latent feature space and
then applies LDA to make predictions. Our results demonstrate that the MM-LDA
networks are significantly more robust to adversarial attacks, and have better
performance in class-biased classification
Approximation Algorithm for Minimum Weight -CDS Problem in Unit Disk Graph
In a wireless sensor network, the virtual backbone plays an important role.
Due to accidental damage or energy depletion, it is desirable that the virtual
backbone is fault-tolerant. A fault-tolerant virtual backbone can be modeled as
a -connected -fold dominating set (-CDS for short). In this paper,
we present a constant approximation algorithm for the minimum weight
-CDS problem in unit disk graphs under the assumption that and
are two fixed constants with . Prior to this work, constant
approximation algorithms are known for with weight and
without weight. Our result is the first constant approximation algorithm for
the -CDS problem with general and with weight. The performance
ratio is for and for ,
where is the performance ratio for the minimum weight -fold
dominating set problem and is the performance ratio for the subset
-connected subgraph problem (both problems are known to have constant
performance ratios.
On Misinformation Containment in Online Social Networks
The widespread online misinformation could cause public panic and serious
economic damages. The misinformation containment problem aims at limiting the
spread of misinformation in online social networks by launching competing
campaigns. Motivated by realistic scenarios, we present the first analysis of
the misinformation containment problem for the case when an arbitrary number of
cascades are allowed. This paper makes four contributions. First, we provide a
formal model for multi-cascade diffusion and introduce an important concept
called as cascade priority. Second, we show that the misinformation containment
problem cannot be approximated within a factor of
in polynomial time unless NP \subseteq
DTIME(n^{\polylog{n}}). Third, we introduce several types of cascade priority
that are frequently seen in real social networks. Finally, we design novel
algorithms for solving the misinformation containment problem. The
effectiveness of the proposed algorithm is supported by encouraging
experimental results.Comment: NIPS 201
Forced field extrapolation of the magnetic structure of the Halpha fibrils in solar chromosphere
We present a careful assess of the forced field extrapolation using Solar
Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) magnetogram.
The convergence property is checked by several metrics. The extrapolated field
lines below 3600km appear to be aligned with most Halpha fibrils observed by
New Vacuum Solar Telescope (NVST). In the region where magnetic energy far
larger than potential energy, field lines computed by forced field
extrapolation still consistent with the patterns of Halpha fibrils while
non-linear force free field (NLFFF) results show large misalignment. The
horizontal average of lorentz force ratio shows the forced region where
force-free assumption is failed can reach the height of . The
non-force-free state of the chromosphere is also confirmed by recent radiation
magnetohydrodynamics (MHD) simulation.Comment: 13pages, 8 figures, Accepted for publication in Ap
Trajectory-based Radical Analysis Network for Online Handwritten Chinese Character Recognition
Recently, great progress has been made for online handwritten Chinese
character recognition due to the emergence of deep learning techniques.
However, previous research mostly treated each Chinese character as one class
without explicitly considering its inherent structure, namely the radical
components with complicated geometry. In this study, we propose a novel
trajectory-based radical analysis network (TRAN) to firstly identify radicals
and analyze two-dimensional structures among radicals simultaneously, then
recognize Chinese characters by generating captions of them based on the
analysis of their internal radicals. The proposed TRAN employs recurrent neural
networks (RNNs) as both an encoder and a decoder. The RNN encoder makes full
use of online information by directly transforming handwriting trajectory into
high-level features. The RNN decoder aims at generating the caption by
detecting radicals and spatial structures through an attention model. The
manner of treating a Chinese character as a two-dimensional composition of
radicals can reduce the size of vocabulary and enable TRAN to possess the
capability of recognizing unseen Chinese character classes, only if the
corresponding radicals have been seen. Evaluated on CASIA-OLHWDB database, the
proposed approach significantly outperforms the state-of-the-art
whole-character modeling approach with a relative character error rate (CER)
reduction of 10%. Meanwhile, for the case of recognition of 500 unseen Chinese
characters, TRAN can achieve a character accuracy of about 60% while the
traditional whole-character method has no capability to handle them
Temperature Dependence of Violation of Bell's Inequality in Coupled Quantum Dots in a Microcavity
Bell's inequality in two coupled quantum dots within cavity QED, including
Forster and exciton-phonon interactions, is investigated theoretically. It is
shown that the environmental temperature has a significant impact on Bell's
inequality
End-to-End Residual CNN with L-GM Loss Speaker Verification System
We propose an end-to-end speaker verification system based on the neural
network and trained by a loss function with less computational complexity. The
end-to-end speaker verification system in this paper consists of a ResNet
architecture to extract features from utterance, then produces utterance-level
speaker embeddings, and train using the large-margin Gaussian Mixture loss
function. Influenced by the large-margin and likelihood regularization,
large-margin Gaussian Mixture loss function benefits the speaker verification
performance. Experimental results demonstrate that the Residual CNN with
large-margin Gaussian Mixture loss outperforms DNN-based i-vector baseline by
more than 10% improvement in accuracy rate.Comment: 5 pages. arXiv admin note: text overlap with arXiv:1803.02988,
arXiv:1705.02304, arXiv:1706.08612 by other author
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