Optimal random sampling designs in random field sampling

A Horvitz-Thompson predictor is proposed for spatial sampling when the characteristic of interest is modeled as a random field. Optimal sampling designs are deduced under this context. Fixed and variable sample size are considered

Dirac Matrices for Chern-Simons Gravity

A genuine gauge theory for the Poincar\'e, de Sitter or anti-de Sitter algebras can be constructed in (2n-1)-dimensional spacetime by means of the Chern-Simons form, yielding a gravitational theory that differs from General Relativity but shares many of its properties, such as second order field equations for the metric. The particular form of the Lagrangian is determined by a rank n, symmetric tensor invariant under the relevant algebra. In practice, the calculation of this invariant tensor can be reduced to the computation of the trace of the symmetrized product of n Dirac Gamma matrices \Gamma_{ab} in 2n-dimensional spacetime. While straightforward in principle, this calculation can become extremely cumbersome in practice. For large enough n, existing computer algebra packages take an inordinate long time to produce the answer or plainly fail having used up all available memory. In this talk we show that the general formula for the trace of the symmetrized product of 2n Gamma matrices \Gamma_{ab} can be written as a certain sum over the integer partitions s of n, with every term being multiplied by a numerical coefficient \alpha_{s}. We then give a general algorithm that computes the \alpha-coefficients as the solution of a linear system of equations generated by evaluating the general formula for different sets of tensors B^{ab} with random numerical entries. A recurrence relation between different coefficients is shown to hold and is used in a second, "minimal" algorithm to greatly speed up the computations. Runtime of the minimal algorithm stays below 1 min on a typical desktop computer for up to n=25, which easily covers all foreseeable applications of the trace formula.Comment: v2: 6 pages, no figures. Based on talk presented at I Cosmosul, Rio de Janeiro, Brazil, August 2011. v3: references adde

OPTIMAL RANDOM SAMPLING DESIGNS IN RANDOM FIELD SAMPLING

A Horvitz-Thompson predictor is proposed for spatial sampling when the characteristic of interest is modeled as a random field. Optimal sampling designs are deduced under this context. Fixed and variable sample size are considered.

Learning in neuro/fuzzy analog chips

This paper focus on the design of adaptive mixed-signal fuzzy chips. These chips have parallel architecture and feature electrically-controlable surface maps. The design methodology is based on the use of composite transistors - modular and well suited for design automation. This methodology is supported by dedicated, hardware-compatible learning algorithms that combine weight-perturbation and outstar

Can fundamentals explain cross-country correlations of asset returns?.

Previous studies show that existing correlations between national returns are higher than correlations between the national growth rates of fundamental variables. This paper examines the ability of intertemporal asset pricing models to explain cross-country correlations of national returns. We find that when capital markets are assumed to be fully integrated, a simple intertemporal general equilibrium model is able to explain the observed co-variability of domestic asset returns but generates too little variability in those returns. Results improve considerably if a less restrictive version is employed. In that setting, both domestic variability and cross-country co-variability of returns are consistent with capital market integration.Asset pricing models; Cross-country correlations;

Modular Design of Adaptive Analog CMOS Fuzzy Controller Chips

Analog circuits are natural candidates to design fuzzy chips with optimum speed/power figures for precision up to about 1%. This paper presents a methodology and circuit blocks to realize fuzzy controllers in the form of analog CMOS chips. These chips can be made to adapt their function through electrical control. The proposed design methodology emphasizes modularity and simplicity at the circuit level -- prerequisites to increasing processor complexity and operation speed. The paper include measurements from a silicon prototype of a fuzzy controller chip in CMOS 1.5μm single-poly technology

Using Building Blocks to Design Analog Neuro-Fuzzy Controllers

We present a parallel architecture for fuzzy controllers and a methodology for their realization as analog CMOS chips for low- and medium-precision applications. These chips can be made to learn through the adaptation of electrically controllable parameters guided by a dedicated hardware-compatible learning algorithm. Our designs emphasize simplicity at the circuit level—a prerequisite for increasing processor complexity and operation speed. Examples include a three-input, four-rule controller chip in 1.5-μm CMOS, single-poly, double-metal technology