Hierarchical Structure Formation and Chemical Evolution of Damped Ly alpha Systems

We present a model for chemical evolution of damped Ly alpha systems considering production of metals by SNe II and infall associated with hierarchical structure formation. The growth of metallicity in these systems is a reflection of the competition between astration and infall. The apparent late turn-on of these systems is due to the late cut-off of infall. The wide range in [Fe/H] at a given redshift is explained by the range of the times for onset of star formation and the range of the times for infall cessation in different systems. The observed lower bound of [Fe/H] = -3 follows from the very rapid initial rise of [Fe/H] subsequent to onset of star formation. To reach [Fe/H] = -3 from a metal-free initial state requires only about 30 Myr so that the probability of observing lower [Fe/H] values is very small.Comment: 4 pages, 2 figures, to appear in ApJ

A Functional Approach to FBSDEs and Its Application in Optimal Portfolios

In Liang et al (2009), the current authors demonstrated that BSDEs can be reformulated as functional differential equations, and as an application, they solved BSDEs on general filtered probability spaces. In this paper the authors continue the study of functional differential equations and demonstrate how such approach can be used to solve FBSDEs. By this approach the equations can be solved in one direction altogether rather than in a forward and backward way. The solutions of FBSDEs are then employed to construct the weak solutions to a class of BSDE systems (not necessarily scalar) with quadratic growth, by a nonlinear version of Girsanov's transformation. As the solving procedure is constructive, the authors not only obtain the existence and uniqueness theorem, but also really work out the solutions to such class of BSDE systems with quadratic growth. Finally an optimal portfolio problem in incomplete markets is solved based on the functional differential equation approach and the nonlinear Girsanov's transformation.Comment: 26 page

Knowledge Refinement via Rule Selection

In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size

A Test of the Balassa-Samuelson Effect Applied to Chinese Regional Data

In this paper, we investigate the relevance of the Balassa-Samuelson effect to the determination of regional inflation in China, for the period 1985 – 2000. To do this, we first construct annual measures of Chinese inflation and industry input on regional and sectoral basis. Then we generalise the Asea and Mendoza (1994) settings to consider asymmetric productivity shocks across sectors. Testing this model on Chinese Regional Data aid of non-stationary panel data techniques, it shows that our extended theoretical model is a good empirical representation of the Chinese data that supports the Balassa-Samuelson effect. Moreover, we are able to test the Asea and Mendoza (1994) version of our general model and find that the restrictions are rejected.Balassa-Samuelson effect, productivity shocks, panel data