On the Stacking Charge Order in NaV2O5

We propose a mechanism for the observed stacking charge order in the quarter-filled ladder compound NaV2O5. Via a standard mapping of the charge degrees of freedom onto Ising spins we explain the stacking order as a result of competition between couplings of the nearest and next-nearest planes with the 4-fold degenerate super-antiferroelectric in-plane order.Comment: 4 pages, 5 figure

Attractor Metadynamics in Adapting Neural Networks

Slow adaption processes, like synaptic and intrinsic plasticity, abound in the brain and shape the landscape for the neural dynamics occurring on substantially faster timescales. At any given time the network is characterized by a set of internal parameters, which are adapting continuously, albeit slowly. This set of parameters defines the number and the location of the respective adiabatic attractors. The slow evolution of network parameters hence induces an evolving attractor landscape, a process which we term attractor metadynamics. We study the nature of the metadynamics of the attractor landscape for several continuous-time autonomous model networks. We find both first- and second-order changes in the location of adiabatic attractors and argue that the study of the continuously evolving attractor landscape constitutes a powerful tool for understanding the overall development of the neural dynamics

The Spin-SAF transition in NaV2O5 induced by spin-pseudospin coupling

We present microscopic estimates for the spin-spin and spin-speudospin interactions of the quarter-filled ladder compound NaV2O5, obtained by exactly diagonalizing appropriate clusters of the underlying generalized Hubbard Hamiltonian. We present evidence for a substantial interladder spin-pseudospin interaction term which would allow simultaneously for the superantiferroelectric (SAF) charge (pseudospin) ordering and spin dimerization. We discuss the values of the coupling constants appropriate for NaV2O5 and deduce the absence of a soft antiferroelectric mode

Properties of the energetic particle distributions during the October 28, 2003 solar flare from INTEGRAL/SPI observations

Analysis of spectra obtained with the gamma-ray spectrometer SPI onboard INTEGRAL of the GOES X17-class flare on October 28, 2003 is presented. In the energy range 600 keV - 8 MeV three prominent narrow lines at 2.223, 4.4 and 6.1 MeV, resulting from nuclear interactions of accelerated ions within the solar atmosphere could be observed. Time profiles of the three lines and the underlying continuum indicate distinct phases with several emission peaks and varying continuum-to-line ratio for several minutes before a smoother decay phase sets in. Due to the high-resolution Ge detectors of SPI and the exceptional intensity of the flare, detailed studies of the 4.4 and 6.1 MeV line shapes was possible for the first time. Comparison with calculated line shapes using a thick target interaction model and several energetic particle angular distributions indicates that the nuclear interactions were induced by downward-directed particle beams with alpha-to-proton ratios of the order of 0.1. There are also indications that the 4.4 MeV to 6.1 MeV line fluence ratio changed between the beginning and the decay phase of the flare, possibly due to a temporal evolution of the energetic particle alpha-to-proton ratio.Comment: 24 pages, 10 figures, accepted for publication by A&

Quantum antiferromagnetism and high TCT_C superconductivity: a close connection between the t-J model and the projected BCS Hamiltonian

A connection between quantum antiferromagnetism and high $T_C$ superconductivity is theoretically investigated by analyzing the t-J model and its relationships to the Gutzwiller-projected BCS Hamiltonian. After numerical corroboration via exact diagonalization, it is analytically shown that the ground state of the t-J model at half filling (i.e., the 2D antiferromagnetic Heisenberg model) is entirely equivalent to the ground state of the Gutzwiller-projected BCS Hamiltonian with strong pairing. Combined with the high wavefunction overlap between the ground states of the t-J model and the projected BCS Hamiltonian at moderate doping, this equivalence provides strong support for the existence of superconductivity in the t-J model. The relationship between the ground state of the projected BCS Hamiltonian and Anderson's resonating valence bond state (i.e., the projected BCS ground state) is discussed.Comment: 18 pages, 9 figures, the final version published in Phys. Rev.

Effects of in-chain and off-chain substitutions on spin fluctuations in the spin-Peierls compound CuGeO_3

The effect of in-chain and off-chain substitutions on 1D spin fluctuations in the spin-Peierls compound CuGeO_3 has been studied using Raman scattering in order to understand the interplay between defect induced states, enhanced spin-spin correlations and the ground state of low dimensional systems. In-chain and off-chain substitutions quench the spin-Peierls state and induce 3D antiferromagnetic order at T\leq 5 K. Consequently a suppression of a 1D gap-induced mode as well as a constant intensity of a spinon continuum are observed at low temperatures. A 3D two-magnon density of states now gradually extends to higher temperatures T\leq 60K compared with pure CuGeO_3. This effect is more pronounced in the case of off-chain substitutions (Si) for which a N\'eel state occurs over a larger substitution range, starting at very low concentrations. Besides, additional low energy excitations are induced. These effects, i.e. the shift of a dimensional crossover to higher temperatures are due to an enhancement of the spin-spin correlations induced by a small amount of substitutions. The results are compared with recent Monte Carlo studies on substituted spin ladders, pointing to a similar instability of coupled, dimerized spin chains and spin ladders upon substitution.Comment: 14 pages, 6 eps figures, to be published in PR

Automated quantification and evaluation of motion artifact on coronary CT angiography images

Abstract Purpose This study developed and validated a Motion Artifact Quantification algorithm to automatically quantify the severity of motion artifacts on coronary computed tomography angiography (CCTA) images. The algorithm was then used to develop a Motion IQ Decision method to automatically identify whether a CCTA dataset is of sufficient diagnostic image quality or requires further correction. Method The developed Motion Artifact Quantification algorithm includes steps to identify the right coronary artery (RCA) regions of interest (ROIs), segment vessel and shading artifacts, and to calculate the motion artifact score (MAS) metric. The segmentation algorithms were verified against ground‐truth manual segmentations. The segmentation algorithms were also verified by comparing and analyzing the MAS calculated from ground‐truth segmentations and the algorithm‐generated segmentations. The Motion IQ Decision algorithm first identifies slices with unsatisfactory image quality using a MAS threshold. The algorithm then uses an artifact‐length threshold to determine whether the degraded vessel segment is large enough to cause the dataset to be nondiagnostic. An observer study on 30 clinical CCTA datasets was performed to obtain the ground‐truth decisions of whether the datasets were of sufficient image quality. A five‐fold cross‐validation was used to identify the thresholds and to evaluate the Motion IQ Decision algorithm. Results The automated segmentation algorithms in the Motion Artifact Quantification algorithm resulted in Dice coefficients of 0.84 for the segmented vessel regions and 0.75 for the segmented shading artifact regions. The MAS calculated using the automated algorithm was within 10% of the values obtained using ground‐truth segmentations. The MAS threshold and artifact‐length thresholds were determined by the ROC analysis to be 0.6 and 6.25 mm by all folds. The Motion IQ Decision algorithm demonstrated 100% sensitivity, 66.7% ± 27.9% specificity, and a total accuracy of 86.7% ± 12.5% for identifying datasets in which the RCA required correction. The Motion IQ Decision algorithm demonstrated 91.3% sensitivity, 71.4% specificity, and a total accuracy of 86.7% for identifying CCTA datasets that need correction for any of the three main vessels. Conclusion The Motion Artifact Quantification algorithm calculated accurate