4,024 research outputs found
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 , 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
double point group. It is shown that all bands of these nanowires are doubly
degenerate at the -point and some of these bands will split into
non-degenerate bands when the wave vector moves away from the
-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 -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
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