Holographic Shear Viscosity in Hyperscaling Violating Theories without Translational Invariance

In this paper we investigate the ratio of shear viscosity to entropy density, $\eta/s$, in hyperscaling violating geometry with lattice structure. We show that the scaling relation with hyperscaling violation gives a strong constraint to the mass of graviton and usually leads to a power law of temperature, $\eta/s\sim T^\kappa$. We find the exponent $\kappa$ can be greater than two such that the new bound for viscosity raised in arXiv:1601.02757 is violated. Our above observation is testified by constructing specific solutions with UV completion in various holographic models. Finally, we compare the boundedness of $\kappa$ with the behavior of entanglement entropy and conjecture a relation between them.Comment: 38 pages, 8 figures: 1 appendix added, 2 figures added, 1 references adde

Basis Expansions for Functional Snippets

Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. In this paper, we investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly (and often much) shorter than the length of the whole interval of interest. For such a sampling plan, no data is available for direct estimation of the off-diagonal region of the covariance function. We tackle this challenge via a basis representation of the covariance function. The proposed approach allows one to consistently estimate an infinite-rank covariance function from functional snippets. We establish the convergence rates for the proposed estimators and illustrate their finite-sample performance via simulation studies and two data applications.Comment: 51 pages, 10 figure

Holographic Metal-Insulator Transition in Higher Derivative Gravity

We introduce a Weyl term into the Einstein-Maxwell-Axion theory in four dimensional spacetime. Up to the first order of the Weyl coupling parameter $\gamma$, we construct charged black brane solutions without translational invariance in a perturbative manner. Among all the holographic frameworks involving higher derivative gravity, we are the first to obtain metal-insulator transitions (MIT) when varying the system parameters at zero temperature. Furthermore, we study the holographic entanglement entropy (HEE) of strip geometry in this model and find that the second order derivative of HEE with respect to the axion parameter exhibits maximization behavior near quantum critical points (QCPs) of MIT. It testifies the conjecture in 1502.03661 and 1604.04857 that HEE itself or its derivatives can be used to diagnose quantum phase transition (QPT).Comment: 20 pages, 4 figures; typo corrected, added 3 references; minor revisio

Vowel Creation by Articulatory Control in HMM-based Parametric Speech Synthesis

This paper presents a method to produce a new vowel by articulatory control in hidden Markov model (HMM) based parametric speech synthesis. A multiple regression HMM (MRHMM) is adopted to model the distribution of acoustic features, with articulatory features used as external auxiliary variables. The dependency between acoustic and articulatory features is modelled by a group of linear transforms that are either estimated context-dependently or determined by the distribution of articulatory features. Vowel identity is removed from the set of context features used to ensure compatibility between the contextdependent model parameters and the articulatory features of a new vowel. At synthesis time, acoustic features are predicted according to the input articulatory features as well as context information. With an appropriate articulatory feature sequence, a new vowel can be generated even when it does not exist in the training set. Experimental results show this method is effective in creating the English vowel /2 / by articulatory control without using any acoustic samples of this vowel

Robustness of HMM-based Speech Synthesis

As speech synthesis techniques become more advanced, we are able to consider building high-quality voices from data collected outside the usual highly-controlled recording studio environment. This presents new challenges that are not present in conventional text-to-speech synthesis: the available speech data are not perfectly clean, the recording conditions are not consistent, and/or the phonetic balance of the material is not ideal. Although a clear picture of the performance of various speech synthesis techniques (e.g., concatenative, HMM-based or hybrid) under good conditions is provided by the Blizzard Challenge, it is not well understood how robust these algorithms are to less favourable conditions. In this paper, we analyse the performance of several speech synthesis methods under such conditions. This is, as far as we know, a new research topic: ``Robust speech synthesis.'' As a consequence of our investigations, we propose a new robust training method for the HMM-based speech synthesis in for use with speech data collected in unfavourable conditions

Integrating Articulatory Features into HMM-based Parametric Speech Synthesis

This paper presents an investigation of ways to integrate articulatory features into Hidden Markov Model (HMM)-based parametric speech synthesis, primarily with the aim of improving the performance of acoustic parameter generation. The joint distribution of acoustic and articulatory features is estimated during training and is then used for parameter generation at synthesis time in conjunction with a maximum-likelihood criterion. Different model structures are explored to allow the articulatory features to influence acoustic modeling: model clustering, state synchrony and cross-stream feature dependency. The results of objective evaluation show that the accuracy of acoustic parameter prediction can be improved when shared clustering and asynchronous-state model structures are adopted for combined acoustic and articulatory features. More significantly, our experiments demonstrate that modeling the dependency between these two feature streams can make speech synthesis more flexible. The characteristics of synthetic speech can be easily controlled by modifying generated articulatory features as part of the process of acoustic parameter generation