The mbsts package: Multivariate Bayesian Structural Time Series Models in R

The multivariate Bayesian structural time series (MBSTS) model \citep{qiu2018multivariate,Jammalamadaka2019Predicting} as a generalized version of many structural time series models, deals with inference and prediction for multiple correlated time series, where one also has the choice of using a different candidate pool of contemporaneous predictors for each target series. The MBSTS model has wide applications and is ideal for feature selection, time series forecasting, nowcasting, inferring causal impact, and others. This paper demonstrates how to use the R package \pkg{mbsts} for MBSTS modeling, establishing a bridge between user-friendly and developer-friendly functions in package and the corresponding methodology. A simulated dataset and object-oriented functions in the \pkg{mbsts} package are explained in the way that enables users to flexibly add or deduct some components, as well as to simplify or complicate some settings

Electronic structures of [111]-oriented free-standing InAs and InP nanowires

We report on a theoretical study of the electronic structures of the [111]-oriented, free-standing, zincblende InAs and InP nanowires with hexagonal cross sections by means of an atomistic $sp^{3}s^{*}$, spin-orbit interaction included, nearest-neighbor, tight-binding method. The band structures and the band state wave functions of these nanowires are calculated and the symmetry properties of the bands and band states are analyzed based on the $C_{3v}$ double point group. It is shown that all bands of these nanowires are doubly degenerate at the $\Gamma$-point and some of these bands will split into non-degenerate bands when the wave vector $k$ moves away from the $\Gamma$-point as a manifestation of spin-splitting due to spin-orbit interaction. It is also shown that the lower conduction bands of these nanowires all show simple parabolic dispersion relations, while the top valence bands show complex dispersion relations and band crossings. The band state wave functions are presented by the spatial probability distributions and it is found that all the band states show $2\pi/3$-rotation symmetric probability distributions. The effects of quantum confinement on the band structures of the [111]-oriented InAs and InP nanowires are also examined and an empirical formula for the description of quantization energies of the lowest conduction band and the highest valence band is presented. The formula can simply be used to estimate the enhancement of the band gaps of the nanowires at different sizes as a result of quantum confinement.Comment: 9 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:1502.0756

Performance of Spatial Diversity DCO-OFDM in a Weak Turbulence Underwater Visible Light Communication Channel

The performance of underwater visible light communication (UVLC) system is severely affected by absorption, scattering and turbulence. In this article, we study the performance of spectral efficient DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) in combination with the transceiver spatial diversity in turbulence channel. Based on the approximation of the weighted sum of lognormal random variables (RVs), we derived a theoretical exact bit error rate (BER) for DCO-OFDM systems with spatial diversity. The simulation results are compared with the analytical prediction, confirming the validity of the analysis. It is shown that spatial diversity can effectively reduce the turbulence-induced channel fading. The obtained results can be useful for designing, predicting, and evaluating the DCO-OFDM UVLC system in a weak oceanic turbulence condition

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

Comparator design automation in SEAS

The authors describe the development and realization of a synthesis program for CMOS comparators. It was constructed by following the SEAS framework for analog circuit design automation. An example of modeling and synthesis is shown. Using this program, a number of comparators were synthesized, and they were tested by using SPICE simulations. All comparators generated performed well above their design specifications. This shows that the SEAS concept is also applicable to the synthesis of nonlinear circuits