Effective Edge State Dynamics in the Fractional Quantum Hall Effect

(2018)

Authors:

Richard Fern, Roberto Bondesan, Steven H Simon

Emergence and spontaneous breaking of approximate O(4) symmetry at a weakly first-order deconfined phase transition

(2018)

Authors:

Pablo Serna, Adam Nahum

Clustering of magnetic swimmers in a Poiseuille flow

Physical Review Letters American Physical Society 120:18 (2018) 188101

Authors:

Fanlong Meng, Daiki Matsunaga, Ramin Golestanian

Abstract:

We investigate the collective behavior of magnetic swimmers, which are suspended in a Poiseuille flow and placed under an external magnetic field, using analytical techniques and Brownian dynamics simulations. We find that the interplay between intrinsic activity, external alignment, and magnetic dipole-dipole interactions leads to longitudinal structure formation. Our work sheds light on a recent experimental observation of a clustering instability in this system.

Einstein–Bose condensation of Onsager vortices

New Journal of Physics IOP Publishing 20:5 (2018) 053038

Authors:

Rahil N Valani, Andrew J Groszek, Tapio P Simula

Comparison of cumulant expansion and q-space imaging estimates for diffusional kurtosis in brain.

Magnetic resonance imaging 48 (2018) 80-88

Authors:

Vaibhav Mohanty, Emilie T McKinnon, Joseph A Helpern, Jens H Jensen

Abstract:

Purpose

To compare estimates for the diffusional kurtosis in brain as obtained from a cumulant expansion (CE) of the diffusion MRI (dMRI) signal and from q-space (QS) imaging.

Theory and methods

For the CE estimates of the kurtosis, the CE was truncated to quadratic order in the b-value and fit to the dMRI signal for b-values from 0 up to 2000s/mm2. For the QS estimates, b-values ranging from 0 up to 10,000s/mm2 were used to determine the diffusion displacement probability density function (dPDF) via Stejskal's formula. The kurtosis was then calculated directly from the second and fourth order moments of the dPDF. These two approximations were studied for in vivo human data obtained on a 3T MRI scanner using three orthogonal diffusion encoding directions.

Results

The whole brain mean values for the CE and QS kurtosis estimates differed by 16% or less in each of the considered diffusion encoding directions, and the Pearson correlation coefficients all exceeded 0.85. Nonetheless, there were large discrepancies in many voxels, particularly those with either very high or very low kurtoses relative to the mean values.

Conclusion

Estimates of the diffusional kurtosis in brain obtained using CE and QS approximations are strongly correlated, suggesting that they encode similar information. However, for the choice of b-values employed here, there may be substantial differences, depending on the properties of the diffusion microenvironment in each voxel.