Magnetic and Dynamic Properties of the Hubbard Model in Infinite Dimensions

An essentially exact solution of the infinite dimensional Hubbard model is made possible by using a self-consistent mapping of the Hubbard model in this limit to an effective single impurity Anderson model. Solving the latter with quantum Monte Carlo procedures enables us to obtain exact results for the one and two-particle properties of the infinite dimensional Hubbard model. In particular we find antiferromagnetism and a pseudogap in the single-particle density of states for sufficiently large values of the intrasite Coulomb interaction at half filling. Both the antiferromagnetic phase and the insulating phase above the N\'eel temperature are found to be quickly suppressed on doping. The latter is replaced by a heavy electron metal with a quasiparticle mass strongly dependent on doping as soon as $n<1$. At half filling the antiferromagnetic phase boundary agrees surprisingly well in shape and order of magnitude with results for the three dimensional Hubbard model.Comment: 32 page

Model Analysis of Time Reversal Symmetry Test in the Caltech Fe-57 Gamma-Transition Experiment

The CALTECH gamma-transition experiment testing time reversal symmetry via the E2/M1 mulipole mixing ratio of the 122 keV gamma-line in Fe-57 has already been performed in 1977. Extending an earlier analysis in terms of an effective one-body potential, this experiment is now analyzed in terms of effective one boson exchange T-odd P-even nucleon nucleon potentials. Within the model space considered for the Fe-57 nucleus no contribution from isovector rho-type exchange is possible. The bound on the coupling strength phi_A from effective short range axial-vector type exchange induced by the experimental bound on sin(eta) leads to phi_A < 10^{-2}.Comment: 5 pages, RevTex 3.

Inflation, cold dark matter, and the central density problem

A problem with high central densities in dark halos has arisen in the context of LCDM cosmologies with scale-invariant initial power spectra. Although n=1 is often justified by appealing to the inflation scenario, inflationary models with mild deviations from scale-invariance are not uncommon and models with significant running of the spectral index are plausible. Even mild deviations from scale-invariance can be important because halo collapse times and densities depend on the relative amount of small-scale power. We choose several popular models of inflation and work out the ramifications for galaxy central densities. For each model, we calculate its COBE-normalized power spectrum and deduce the implied halo densities using a semi-analytic method calibrated against N-body simulations. We compare our predictions to a sample of dark matter-dominated galaxies using a non-parametric measure of the density. While standard n=1, LCDM halos are overdense by a factor of 6, several of our example inflation+CDM models predict halo densities well within the range preferred by observations. We also show how the presence of massive (0.5 eV) neutrinos may help to alleviate the central density problem even with n=1. We conclude that galaxy central densities may not be as problematic for the CDM paradigm as is sometimes assumed: rather than telling us something about the nature of the dark matter, galaxy rotation curves may be telling us something about inflation and/or neutrinos. An important test of this idea will be an eventual consensus on the value of sigma_8, the rms overdensity on the scale 8 h^-1 Mpc. Our successful models have values of sigma_8 approximately 0.75, which is within the range of recent determinations. Finally, models with n>1 (or sigma_8 > 1) are highly disfavored.Comment: 13 pages, 6 figures. Minor changes made to reflect referee's Comments, error in Eq. (18) corrected, references updated and corrected, conclusions unchanged. Version accepted for publication in Phys. Rev. D, scheduled for 15 August 200

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Multiple brain atlas database and atlas-based neuroimaging system

10.1002/(SICI)1097-0150(1997)2:13.0.CO;2-NComputer Aided Surgery2142-66CAIS