Scaling of the turbulence transition threshold in a pipe

We report the results of an experimental investigation of the transition to turbulence in a pipe over approximately an order of magnitude range in $Re$. A novel scaling law is uncovered using a systematic experimental procedure which permits contact to be made with modern theoretical thinking. The principal result we uncover is a scaling law which indicates that the amplitude of perturbation required to cause transition scales as $O(Re^{-1})$.Comment: 4 pages, RevTex (submitted to Phys. Rev. Lett.

Viscous fingering and dendritic growth under an elastic membrane

We investigate the viscous fingering instability that arises when air is injected from the end of an oil-filled, compliant channel. We show that induced axial and transverse depth gradients foster novel pattern formation. Moreover, the steady propagation of the interface allows us to elucidate the nonlinear saturation of a fingering pattern first observed in a time-evolving system (Pihler-Puzovic et al. PRL 108, 074502, 2012): the wavelength is set by the viscous fingering mechanism, but the amplitude is inversely proportional to the tangent of the compliant wall's inclination angle

When is an action caused from within? Quantifying the causal chain leading to actions in simulated agents

An agent's actions can be influenced by external factors through the inputs it receives from the environment, as well as internal factors, such as memories or intrinsic preferences. The extent to which an agent's actions are "caused from within", as opposed to being externally driven, should depend on its sensor capacity as well as environmental demands for memory and context-dependent behavior. Here, we test this hypothesis using simulated agents ("animats"), equipped with small adaptive Markov Brains (MB) that evolve to solve a perceptual-categorization task under conditions varied with regards to the agents' sensor capacity and task difficulty. Using a novel formalism developed to identify and quantify the actual causes of occurrences ("what caused what?") in complex networks, we evaluate the direct causes of the animats' actions. In addition, we extend this framework to trace the causal chain ("causes of causes") leading to an animat's actions back in time, and compare the obtained spatio-temporal causal history across task conditions. We found that measures quantifying the extent to which an animat's actions are caused by internal factors (as opposed to being driven by the environment through its sensors) varied consistently with defining aspects of the task conditions they evolved to thrive in.Comment: Submitted and accepted to Alife 2019 conference. Revised version: edits include adding more references to relevant work and clarifying minor points in response to reviewer