Global Optimization in Any Minkowski Metric: A Permutation-Translation Simulated Annealing Algorithm for Multidimensional Scaling

It is well known that considering a non-Euclidean Minkowski metric in Multidimensional Scaling, either for the distance model or for the loss function, increases the computational problem of local minima considerably. In this paper, we propose an algorithm in which both the loss function and the composition rule can be considered in any Minkowski metric, using a multivariate randomly alternating Simulated Annealing procedure with permutation and translation phases. The algorithm has been implemented in Fortran and tested over classical and simulated data matrices with sizes up to 200 objects. A study has been carried out with some of the common loss functions to determine the most suitable values for the main parameters. The experimental results confirm the theoretical expectation that Simulated Annealing is a suitable strategy to deal by itself with the optimization problems in Multidimensional Scaling, in particular for City-Block, Euclidean and Infinity metrics.     

Forecasting volatility by means of threshold models

The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out‐of‐sample forecasting ability of the SETAR‐Threshold GARCH (SETAR‐TGARCH) and the SETAR‐Threshold Stochastic Volatility (SETAR‐THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ‘true’ underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR‐THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd.     

Weight measurement using image-based pose analysis

Image-based gait analysis as a means of biometric identification has attracted much research attention. Most of the existing methods focus on human identification, posture analysis and movement tracking. There have been few investigations on measuring the carried load based on the carrier＇s gait characteristics by automatic image processing. Nevertheless, this measurement is very useful in a number of applications, such as the study of the carried load on the postural development of children and adolescence. In this paper, we investigate how to automatically estimate the carried weight from a sequence of images. We present a method to extract human gait silhouette based on an observation that humans tend to minimize the energy during motion. We compute several angles of body leaning and determine the relationship of the carried weight, the leaning angles and the centroid location according to a human kinetic study. Our weight determination method has been verified successfully by experiments.     

Sister chromatid exchanges in lymphocytes of normal and alcoholic subjects.

The effects of alcohol consumption, cigarette smoking and age on sister chromatid exchange (SCE) frequency in human lymphocytes were assessed by means of multiple linear regression. An increase in SCE rates was associated with alcohol consumption (p = 0.0001), smoking (p = 0.0231), and, to a small extent (p = 0.057), age. These three confounding factors explain 48% of the inter-personal variation in SCE rates among subjects studied.