Why third infinity?

Assemblies of interacting self-driven entities form soft active materials with intriguing collective behavior and mechanical properties. Examples abound in nature on many scales, from the flocking of birds to cell migration in morphogenesis. They also include synthetic systems, from engineered microswimmers to self-catalytic colloids and autonomously propelled liquid crystals. What unifies these systems is that they are driven out of equilibrium by dissipative processes that act on each individual particle, hence break the time reversal symmetry of the dynamics at the microscale. This results in surprising behavior. For instance, active fluids flow with no externally applied driving forces, active gases do not fill their container, and active particles spontaneously organize when passive ones would not. In this talk I will discuss the physics of active matter with examples from both the living and non-living worlds. I will show that by combining minimal physical models with continuum theory and simulations we are making advances towards capturing quantitatively the laws of spontaneous organization of active systems. This theoretical progress has implication for both formulating design principles for new smart materials and understanding cellular and multicellular organization.

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Soft matter is very susceptible to external stimuli, as ‘soft’ suggests. Already small forces or modest potentials can significantly affect the behaviour of soft matter. I will start by explaining how this is related to the infinities and how this especially affects the third infinity. Then, this characteristic behaviour will be illustrated with two examples. In both cases, we apply external stimuli and observe the response on all relevant length scales using conventional and confocal microscopy. First, the effects of mechanical deformation (shear) on the structure and dynamics of colloidal hard spheres is investigated. Second, an external potential is imposed on the colloids by exposing them to an extended modulated light field. In particular the effect of the potential on the particle dynamics is quantitatively and systematically studied.

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For the macroscopic world, classical thermodynamics formulates the laws governing the transformation of various forms of energy into each other. Stochastic thermodynamics extends these concepts to micro- and nano-systems embedded or coupled to a heat bath where fluctuations play a dominant role. Examples are colloidal particles in time-dependent laser traps, single biomolecules manipulated by optical tweezers or AFM tips, and transport through quantum dots. For these systems, exact non-equilibrium relations like the Jarzynski relation, fluctuation theorems and, most recently, a thermodynamic uncertainty relation have been discovered. First, I will introduce the main principles and show a few representative experimental applications. In the second part, I will discuss the universal trade-off between the thermodynamic cost and the precision of any biomolecular, or, more generally, of any stationary non-equilibrium process. By applying this thermodynamic uncertainty relation to molecular motors, I will introduce the emerging field of "thermodynamic inference" where relations from stochastic thermodynamics are used to infer otherwise yet inaccessible properties of nano-scale systems. I will close with recent insights into the minimal requirements for creating coherent oscillations at finite temperature.

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Past studies of the transition to turbulence in pipes and related shear flows could neither predict the respective critical points for the onset of turbulence nor the nature of this transition. The main difficulty is that in this class of flows turbulence arises despite the linear stability of the laminar flow and that it results from perturbations of finite amplitude. I will show that under such circumstances the transition is driven by a stochastic spreading process: each turbulent spot will eventually die but it may produce offspring beforehand. This setting precisely corresponds to the rules of a stochastic process called ‘directed percolation’. By introducing periodic boundary conditions in laboratory experiments we demonstrate for Couette and pipe flow that the transition to turbulence indeed falls into the directed percolation universality class. In particular I will also show that insights into the transition process can be exploited to control turbulence.

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In the first part of the seminar I will describe neural networks from a statistical-mechanics perspective. Focusing on networks trained for retrieval tasks, I will discuss their complexity and show how it is related to network’s performance: on the one hand, complexity is a desirable feature as it (potentially) increases the amount of information stored in the network, on the other hand, beyond a critical threshold, it dramatically impairs retrieval. Finally, I will introduce an algorithm for information storage, inspired by neurophysiological mechanisms occurring during mammal’s sleep, which yields to an “optimal complexity”.

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Open Access, and more generally Open Science, has been the subject of intensive debates over the past 20 years, providing a new and libre way of publishing, disseminating, and accessing research. In this talk, I will discuss Open Science and Open Access publishing within the physical sciences. I will draw from the Nature Research portfolio of journals, including Communications Physics, and introduce some of the measures supporting Open Science.

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