Deep-layered machines, in which each node computes a Boolean function of all nodes below it, underpin deep learning and digital computation. Yet the statistics of their global output function remain poorly understood. We derive the exact finite-depth distribution of the output of a machine with width $k$ and depth $n$. The distribution depends only on the Hamming weight of the output, and as $n$ increases favors functions with low and high Hamming weights. But this bias peaks at a crossover depth proportional to $2^k$ before collapsing onto the constant functions true and false.

We characterize the quasi-stationary distribution (QSD) of the bond directed-percolation line of the Domany--Kinzel automaton using a matrix-product-state representation of the probability distribution, obtained by projecting out the absorbing state and iterating the transfer matrix. Unlike moment- or sampling-based methods, this yields the full conditional distribution and direct access to information-theoretic diagnostics. The spatial structure of the QSD changes sharply across the transition: the active phase is bulk-like with finite density, whereas in the inactive phase the surviving activity collapses into a single flock occupying a vanishing fraction of the chain, with an internal filling that ranges from a single cluster deep in the inactive phase to a loose, partially filled group near criticality. This picture carries a sharp information-theoretic signature: throughout the inactive phase the bipartite mutual information of the QSD equals the entropy of a single binary choice -- whether the flock lies to the left or right of the cut -- so the surviving clusters together encode just one bit of positional information, corresponding to a single effective cluster. The approach extends matrix-product-state techniques to the projected eigenvector defining a QSD, opening information-theoretic diagnostics for absorbing-state systems that bulk-observable methods cannot reach.

We analyze five-neighbor particle cellular automata whose conventional two-dimensional fundamental diagrams are multivalued, but whose mean flow is uniquely determined by introducing a second density. We first consider binary rules for which the second density is conserved, and then examine rules for which the second density is not conserved but converges asymptotically. These examples give three-dimensional fundamental diagrams in which the mean flow is determined by the particle density and the second density. We then investigate whether this single-valued structure is preserved under real-valued max-plus extensions. There are some rules where two different max-plus extensions are introduced, and numerical simulations show that both extensions preserve the same single-valued three-dimensional fundamental diagram. These observations imply that, in constructing real-valued max-plus extensions, it is important to choose the flux function and the second density consistently.

We confront a meso-scale continuum model, archetypical for the heteroepitaxial context, with an atomistic Vicinal Cellular Automaton (VicCA), built as a homoepitaxial counterpart, to show that in (2+1)D complex surface instabilities are fundamental growth phenomena rather than context-specific artifacts. Our approach is to first construct a Ginzburg-Landau-type model, designed to extend the previously (1+1)D Tersoff-type models in (2+1)D. We complement the continuum approach with a discrete one - the VicCA, in which we use a novel version of the potential landscape for the diffusing particles - a double-well potential located at each step edge. Notably, this framework also reproduces step bunching and step meandering - which are typically treated as incompatible in the theoretical paradigm, but coexist in real material systems. Thus we establish a cross-context correspondence at the level of obtained morphologies and morphology diagrams and, additionally, a multiscale perspective on the governing parameters, bridging the gap between the mesoscale and atomistic modeling.