Equilibration, generalized equipartition, and diffusion in dynamical Lorentz gases

We prove approach to thermal equilibrium for the fully Hamiltonian dynamics of a dynamical Lorentz gas, by which we mean an ensemble of particles moving through a $d$-dimensional array of fixed soft scatterers that each possess an internal harmonic or anharmonic degree of freedom to which moving particles locally couple. We establish that the momentum distribution of the moving particles approaches a Maxwell-Boltzmann distribution at a certain temperature $T$, provided that they are initially fast and the scatterers are in a sufficiently energetic but otherwise arbitrary stationary state of their free dynamics--they need not be in a state of thermal equilibrium. The temperature $T$ to which the particles equilibrate obeys a generalized equipartition relation, in which the associated thermal energy $k_{\mathrm B}T$ is equal to an appropriately defined average of the scatterers' kinetic energy. In the equilibrated state, particle motion is diffusive

Dipole Perturbations of the Reissner-Nordstrom Solution: The Polar Case

The formalism developed by Chandrasekhar for the linear polar perturbations of the Reissner-Nordstrom solution is generalized to include the case of dipole (l=1) perturbations. Then, the perturbed metric coefficients and components of the Maxwell tensor are computed.Comment: 16 pages, LaTeX, no figures. Submitted for publication in Physical Review

Stellar Pulsations excited by a scattered mass

We compute the energy spectra of the gravitational signals emitted when a mass m is scattered by the gravitational field of a star of mass M >> m. We show that, unlike black holes in similar processes, the quasi-normal modes of the star are excited, and that the amount of energy emitted in these modes depends on how close the exciting mass can get to the star.Comment: 23 pages, 6 figures, RevTe

Methods and Approaches for Characterizing Learning Related Changes Observed in functional MRI Data — A Review

Brain imaging data have so far revealed a wealth of information about neuronal circuits involved in higher mental functions like memory, attention, emotion, language etc. Our efforts are toward understanding the learning related effects in brain activity during the acquisition of visuo-motor sequential skills. The aim of this paper is to survey various methods and approaches of analysis that allow the characterization of learning related changes in fMRI data. Traditional imaging analysis using the Statistical Parametric Map (SPM) approach averages out temporal changes and presents overall differences between different stages of learning. We outline other potential approaches for revealing learning effects such as statistical time series analysis, modelling of haemodynamic response function and independent component analysis. We present example case studies from our visuo-motor sequence learning experiments to describe application of SPM and statistical time series analyses. Our review highlights that the problem of characterizing learning induced changes in fMRI data remains an interesting and challenging open research problem

A Multi-disciplinary Approach to the Investigation of Aspects of Serial Order in Cognition

Serial order processing or Sequence processing underlies many human activities such as speech, language, skill learning, planning, problem solving, etc. Investigating the\ud neural bases of sequence processing enables us to understand serial order in cognition and helps us building intelligent devices. In the current paper, various\ud cognitive issues related to sequence processing will be discussed with examples. Some of the issues are: distributed versus local representation, pre-wired versus\ud adaptive origins of representation, implicit versus explicit learning, fixed/flat versus hierarchical organization, timing aspects, order information embedded in sequences, primacy versus recency in list learning and aspects of sequence perception such as recognition, recall and generation. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, theoretical frameworks based on Markov models and Reinforcement Learning paradigm will be presented. These theoretical ideas are useful for studying sequential phenomena in a principled way

Novel pinning phenomena in a superconducting film with a square lattice of artificial pinning centers

We study the transport properties of a superconducting Nb film with a square lattice of artificial pinning centers (APCs) as a function of dc current, at a temperature close to the superconducting transition temperature of the film. We find that, at low dc currents, the differential resistance of the film shows the standard matching field anomaly, that is, the differential resistance has a local minimum at magnetic fields corresponding to an integer number of flux lines per APC. However, at higher dc currents, the differential resistance at each matching field turns to a local maximum, which is exactly opposite to the low current behavior. This novel effect might indicate that the flux lines in the APC system change their flow mode as the dc current is increased.Comment: 10 pages, 4 figure

The spatial correlations in the velocities arising from a random distribution of point vortices

This paper is devoted to a statistical analysis of the velocity fluctuations arising from a random distribution of point vortices in two-dimensional turbulence. Exact results are derived for the correlations in the velocities occurring at two points separated by an arbitrary distance. We find that the spatial correlation function decays extremely slowly with the distance. We discuss the analogy with the statistics of the gravitational field in stellar systems.Comment: 37 pages in RevTeX format (no figure); submitted to Physics of Fluid