On the growth of ammonium nitrate(III) crystals

The growth rate of NH4NO3 phase III crystals is measured and interpreted using two models. The first is a standard crystal growth model based on a spiral growth mechanism, the second outlines the concept of kinetical roughening. As the crystal becomes rough a critical supersaturation can be determined and from this the step free energy. The step free energy versus temperature turns out to be well represented by a Kosterlitz¿Thouless type model. Further a phenomenological treatment of some peculiar growth observations is given

How do sound waves in a Bose-Einstein condensate move so fast?

Low-momentum excitations of a dilute Bose-Einstein condensate behave as phonons and move at a finite velocity v_s. Yet the atoms making up the phonon excitation each move very slowly; v_a = p/m --> 0. A simple "cartoon picture" is suggested to understand this phenomenon intuitively. It implies a relation v_s/v_a = N_ex, where N_ex is the number of excited atoms making up the phonon. This relation does indeed follow from the standard Bogoliubov theory.Comment: 6 pages, 2 figures (.eps), LaTeX2e. More introductory discussion adde

Effective one-component description of two-component Bose-Einstein condensate dynamics

We investigate dynamics in two-component Bose-Einstein condensates in the context of coupled Gross-Pitaevskii equations and derive results for the evolution of the total density fluctuations. Using these results, we show how, in many cases of interest, the dynamics can be accurately described with an effective one-component Gross-Pitaevskii equation for one of the components, with the trap and interaction coefficients determined by the relative differences in the scattering lengths. We discuss the model in various regimes, where it predicts breathing excitations, and the formation of vector solitons. An effective nonlinear evolution is predicted for some cases of current experimental interest. We then apply the model to construct quasi-stationary states of two-component condensates.Comment: 8 pages, 4 figure

Enhanced heat flow in the hydrodynamic-collisionless regime

We study the heat conduction of a cold, thermal cloud in a highly asymmetric trap. The cloud is axially hydrodynamic, but due to the asymmetric trap radially collisionless. By locally heating the cloud we excite a thermal dipole mode and measure its oscillation frequency and damping rate. We find an unexpectedly large heat conduction compared to the homogeneous case. The enhanced heat conduction in this regime is partially caused by atoms with a high angular momentum spiraling in trajectories around the core of the cloud. Since atoms in these trajectories are almost collisionless they strongly contribute to the heat transfer. We observe a second, oscillating hydrodynamic mode, which we identify as a standing wave sound mode.Comment: Sumitted to Phys. Rev. Letters, 4 pages, 4 figure

Nonnormal amplification in random balanced neuronal networks

In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to the network in two distinct ways. One is induced by the presence of near-critical eigenvalues in the connectivity matrix W, producing large but slow activity fluctuations along the corresponding eigenvectors (dynamical slowing). The other relies on W being nonnormal, which allows the network activity to make large but fast excursions along specific directions. Here we investigate the tradeoff between nonnormal amplification and dynamical slowing in the spontaneous activity of large random neuronal networks composed of excitatory and inhibitory neurons. We use a Schur decomposition of W to separate the two amplification mechanisms. Assuming linear stochastic dynamics, we derive an exact expression for the expected amount of purely nonnormal amplification. We find that amplification is very limited if dynamical slowing must be kept weak. We conclude that, to achieve strong transient amplification with little slowing, the connectivity must be structured. We show that unidirectional connections between neurons of the same type together with reciprocal connections between neurons of different types, allow for amplification already in the fast dynamical regime. Finally, our results also shed light on the differences between balanced networks in which inhibition exactly cancels excitation, and those where inhibition dominates.Comment: 13 pages, 7 figure

The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks

Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. In comparison, the functional capabilities of models of spiking networks are still rudimentary. This shortcoming is mainly due to the lack of insight and practical algorithms to construct the necessary connectivity. Any such algorithm typically attempts to build networks by iteratively reducing the error compared to a desired output. But assigning credit to hidden units in multi-layered spiking networks has remained challenging due to the non-differentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity in spiking network models. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients impact learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative’s scale can substantially affect learning performance. When we combine surrogate gradients with a suitable activity regularization technique, robust information processing can be achieved in spiking networks even at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks

Talking science, online

Traditional scientific conferences and seminar events have been hugely disrupted by the COVID-19 pandemic, paving the way for virtual forms of scientific communication to take hold and be put to the test

Reaching the hydrodynamic regime in a Bose-Einstein condensate by suppression of avalanche

We report the realization of a Bose-Einstein condensate (BEC) in the hydrodynamic regime. The hydrodynamic regime is reached by evaporative cooling at a relative low density suppressing the effect of avalanches. With the suppression of avalanches a BEC containing 120.10^6 atoms is produced. The collisional opacity can be tuned from the collisionless regime to a collisional opacity of more than 3 by compressing the trap after condensation. In the collisional opaque regime a significant heating of the cloud at time scales shorter than half of the radial trap period is measured. This is direct proof that the BEC is hydrodynamic.Comment: Article submitted for Phys. Rev. Letters, 6 figure