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 $\Phi_p(x)$. After summing over the four "vertex correction" diagrams, we obtain the results with infrared finiteness which shows that chirally enhanced corrections arise from $\Phi_p(x)$ 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 (k,m)(k,m)-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 $k$-connected $m$-fold dominating set ($(k,m)$-CDS for short). In this paper, we present a constant approximation algorithm for the minimum weight $(k,m)$-CDS problem in unit disk graphs under the assumption that $k$ and $m$ are two fixed constants with $m\geq k$. Prior to this work, constant approximation algorithms are known for $k=1$ with weight and $2\leq k\leq 3$ without weight. Our result is the first constant approximation algorithm for the $(k,m)$-CDS problem with general $k,m$ and with weight. The performance ratio is $(\alpha+2.5k\rho)$ for $k\geq 3$ and $(\alpha+2.5\rho)$ for $k=2$, where $\alpha$ is the performance ratio for the minimum weight $m$-fold dominating set problem and $\rho$ is the performance ratio for the subset $k$-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 $\Omega(2^{\log^{1-\epsilon}n^4})$ 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 $1400-1800km$. 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