Einstein Static Universe in Exponential f(T)f(T) Gravity

We analyze the stability of the Einstein static closed and open universe in two types of exponential $f(T)$ gravity theories. We show that the stable solutions exist in these two models. In particular, we find that large regions of parameter space in equation of state $w=p/\rho$ for the stable universe are allowed in the $f(T)$ theories.Comment: 11 pages, 4 figures, published version with references update

On Adaptive Portfolio Management with Dynamic Black-Litterman Approach

This paper presents a novel framework for adaptive portfolio management that combines a dynamic Black-Litterman optimization with the general factor model and Elastic Net regression. This integrated approach allows us to systematically generate investors' views and mitigate potential estimation errors. Our empirical results demonstrate that this combined approach can lead to computational advantages as well as promising trading performances.Comment: 9 pages, 6 figure

Shape restricted regression with random Bernstein polynomials

Shape restricted regressions, including isotonic regression and concave regression as special cases, are studied using priors on Bernstein polynomials and Markov chain Monte Carlo methods. These priors have large supports, select only smooth functions, can easily incorporate geometric information into the prior, and can be generated without computational difficulty. Algorithms generating priors and posteriors are proposed, and simulation studies are conducted to illustrate the performance of this approach. Comparisons with the density-regression method of Dette et al. (2006) are included.Comment: Published at http://dx.doi.org/10.1214/074921707000000157 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

TEACHING AND PROMOTION ON INQUIRYBASED INSTRUCTIONAL MODULE

The purpose of this study is to design two teaching modules that will enable science teachers to teach laboratory course for students to achieve a meaningful and useful learning. We emphasize students’ self-exploration about science. Each module can be modified for students of different grades (7-12 students) and abilities. The modules greatly enhanced the teacher's own understanding of what he/she wishes the laboratory teaching to achieve. The module contained inquiring activities with explicit teaching of the nature of science. At the same time, these activities can promote their learning motivation; let the students have a better understanding of the science concepts by doing the experiments, and to undergo an experience of learning and reflection by themselves. By observing interesting phenomena and practicing the scientific process skills repeatedly, the modules also efficiently inspired students who lack of learning motivation. This study involves the design of two experimental teaching modules dealing with concepts about animal life, plant life, foraging behavior and social behavior. The designed modules are: 1) Ecosphere experimental teaching module: including photosynthesis, respiration and burning; and 2) Animal behavior ecological observation experimental teaching module: including foraging behavior and social behavior

Profiling time course expression of virus genes---an illustration of Bayesian inference under shape restrictions

There have been several studies of the genome-wide temporal transcriptional program of viruses, based on microarray experiments, which are generally useful in the construction of gene regulation network. It seems that biological interpretations in these studies are directly based on the normalized data and some crude statistics, which provide rough estimates of limited features of the profile and may incur biases. This paper introduces a hierarchical Bayesian shape restricted regression method for making inference on the time course expression of virus genes. Estimates of many salient features of the expression profile like onset time, inflection point, maximum value, time to maximum value, area under curve, etc. can be obtained immediately by this method. Applying this method to a baculovirus microarray time course expression data set, we indicate that many biological questions can be formulated quantitatively and we are able to offer insights into the baculovirus biology.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS258 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org