Spin-Charge Decoupling and Orthofermi Quantum Statistics

Currently Gutzwiller projection technique and nested Bethe ansatz are two main methods used to handle electronic systems in the $U$ infinity limit. We demonstrate that these two approaches describe two distinct physical systems. In the nested Bethe ansatz solutions, there is a decoupling between the spin and charge degrees of freedom. Such a decoupling is absent in the Gutzwiller projection technique. Whereas in the Gutzwiller approach, the usual antisymmetry of space and spin coordinates is maintained, we show that the Bethe ansatz wave function is compatible with a new form of quantum statistics, viz., orthofermi statistics. In this statistics, the wave function is antisymmetric in spatial coordinates alone. This feature ultimately leads to spin-charge decoupling.Comment: 12 pages, LaTex Journal_ref: A slightly abridged version of this paper has appeared as a brief report in Phys. Rev. B, Vol. 63, 132405 (2001

The Role of Components of Demographic Change in Economic Development : Whither the Trend ?

In this paper, we investigate the role of the components of demographic change on economic development. Population growth has both positive and negative effects on income growth. Kelley and Schmidt (1995) states that high birth rates are costly in terms of growth but this effect can be offset by a positive impact of mortality reductions. We study how the weight of each effect has changed over time considering a panel of countries over the last four decades. We find that there is little gain to expect from further reductions in mortality in developing countries, and that the effect of birth rates has become positive in developed countries. In contrast to the earlier study, where growth enhancing effect of population density is felt consistently for all decades, we find that the effect is limited only to the sixties.Demographic components; endogenous growth; panel data

REPRESENTATION-CONSTRAINED CANONICAL CORRELATION-ANALYSIS: A HYBRIDIZATION OF CANONICAL CORRELATION AND PRINCIPAL COMPONENT ANALYSIS

The classical canonical correlation analysis is extremely greedy to maximize the squared correlation between two sets of variables. As a result, if one of the variables in the dataset-1 is very highly correlated with another variable in the dataset-2, the canonical correlation will be very high irrespective of the correlation among the rest of the variables in the two datasets. We intend here to propose an alternative measure of association between two sets of variables that will not permit the greed of a select few variables in the datasets to prevail upon the fellow variables so much as to deprive the latter of contributing to their representative variables or canonical variates. Our proposed Representation-Constrained Canonical correlation (RCCCA) Analysis has the Classical Canonical Correlation Analysis (CCCA) at its one end (t=0) and the Classical Principal Component Analysis (CPCA) at the other (as t tends to be very large). In between it gives us a compromise solution. By a proper choice of t, one can avoid hijacking of the representation issue of two datasets by a lone couple of highly correlated variables across those datasets. This advantage of the RCCCA over the CCCA deserves a serious attention by the researchers using statistical tools for data analysis.Representation, constrained, canonical, correlation, principal components, variates, global optimization, particle swarm, ordinal variables, computer program, FORTRAN versus detection.