We measure the static frictional resistance and the real area of contact between two solid blocks subjected to a normal load. We show that following a two-step change in the normal load the system exhibits nonmonotonic aging and memory effects, two hallmarks of glassy dynamics. These dynamics are strongly influenced by the discrete geometry of the frictional interface, characterized by the attachment and detachment of unique microcontacts. The results are in good agreement with a theoretical model we propose that incorporates this geometry into the framework recently used to describe Kovacs-like relaxation in glasses as well as thermal disordered systems. These results indicate that a frictional interface is a glassy system and strengthen the notion that nonmonotonic relaxation behavior is generic in such systems.

Articular cartilage has well known depth-dependent structure and has recently been shown to have similarly non-uniform depth-dependent mechanical properties. Here, we study anatomic variation of the depth-dependent shear modulus and energy dissipation rate in neonatal bovine knees. The regions we specifically focus on are the patellofemoral groove, trochlea, femoral condyle, and tibial plateau. In every sample, we find a highly compliant region within the first 500 mm of tissue measured from the articular surface, where the local shear modulus is reduced by up to two orders of magnitude. Comparing measurements taken from different anatomic sites, we find statistically significant differences localized within the first 50 mm. Histological images reveal these anatomic variations are associated with differences in collagen density and fiber organization. ß

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In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning. In contrast, elements in an artificial neural network are typically updated simultaneously using a central processor. Here, we investigate the feasibility and effect of desynchronous learning in a recently introduced decentralized, physics-driven learning network. We show that desynchronizing the learning process does not degrade the performance for a variety of tasks in an idealized simulation. In experiment, desynchronization actually improves the performance by allowing the system to better explore the discretized state space of solutions. We draw an analogy between desynchronization and mini-batching in stochastic gradient descent and show that they have similar effects on the learning process. Desynchronizing the learning process establishes physics-driven learning networks as truly fully distributed learning machines, promoting better performance and scalability in deployment.

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