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A fundamental challenge for lightweight architected materials is their propensity for localized failure due to layered buckling, plastic shear-banding or fracture. Recent research efforts have used disorder to interrupt localization and enhance deformation, but most design strategies simply distribute the accumulation of damage, they do not prevent it from developing and propagating. This work explores how gradient architecture can be designed to hinder crack propagation and promote recoverability in nanostructured ceramic metamaterials. We experimentally and numerically investigated five different shell-based spinodal ceramic nanoarchitectures with 10-80 nm thick alumina films. These were fabricated using atomic layer deposition on sacrificial polymeric scaffolds written using two-photon lithography. All thin-walled (<40 nm) architectures underwent shell buckling-dominated deformation and showed nearly full recovery after compression to 45% strain, an expected result for this class of nanoarchitected materials. Thick-walled (>40 nm) isotropic and anisotropic architectures experienced considerable local damage during compression and predictably showed permanent failure even at low strains. Unexpectedly, thick-walled conch-shell inspired gradient architectures showed localized damage but experienced a full recovery after compression to 45% strain. This degree of recoverability has never been observed in this high density of a nanostructured ceramic, particularly one with visible local cracking during compression. This result stems from the length scale of the structural heterogeneity - the gradient layers were sufficiently small so as to inhibit the local damage development needed for crack propagation, thereby preventing catastrophic failure. Our findings have significant implications for how length scales and heterogeneity can be used to design failure-resistant materials from brittle constituents. 

Agreement is central to the morphosyntax of  many natural languages. Within contemporary linguistic theory, agreement relations have often been analyzed as the result of a structure-sensitive search operation. Neural language models, which lack an explicit bias for this type of operation, have shown mixed success at capturing morphosyntactic agreement phenomena. This paper develops an alternative neural model that formalizes the search operation in a fully differentiable way using gradient neural attention, and evaluates the model's ability to learn the complex agreement system of Hindi-Urdu from a large-scale dependency treebank and smaller synthetic datasets. We find that this model outperforms standard architectures at generalizing agreement patterns to held-out examples and structures. 

According to the influential continuum model of phonation, only voiced segments can be specified as creaky or breathy. The present study investigated many possible phonetic correlates of the laryngeal contrast in Javanese word-initial prevocalic stop consonants, drawing upon a spoken corpus of more than 180,000 utterances. The results indicate that the laryngeal contrast is cued by voice onset time (VOT) and several acoustic-phonetic properties of the following vowel, including the first formant (F1) in addition to voice source measurements such as H1*-H2* and cepstral peak prominence (CPP). Taken together these findings indicate that Javanese stops can be both voiceless and breathy, supporting a revision of the continuum model in which voicing and other aspects of phonation are decoupled. 

In Harmonic Grammar and Optimality Theory, well-formed representations are those that optimally satisfy a set of violable constraints, as determined by candidate comparison under a given weighting or ranking. This paper develops variants of HG/OT in which conflict among violable constraints plays out locally, within each part of a representation, rather than through optimization over alternatives. Static HG/OT has a simple formal definition that has important precedents in classic and modern neural networks and that restricts the logic expressivity of constraint-interaction grammars. The static approach to constraint conflict is illustrated for local segmental phonology (the distribution of vowel height in Cochabamba Quechua) and unbounded feature spreading (nasal harmony as in Johore Malay). A Python implementation of the theory and several demonstrations are available at https://github.com/colincwilson/statgram. 

Morphological patterns can involve simple concatenation of fixed strings (e.g., unkind, kindness) or ‘nonconcatenative’ processes such as infixation (e.g., Chamorro l-um-iʔeʔ ‘saw (actor-focus)’, Topping, 1973) and reduplication (e.g., Amele ba-bagawen ‘as he came out’, Roberts, 1987), among many others (e.g., Anderson, 1992; Inkelas, 2014). Recent work has established that deep neural networks are capable of inducing both concatenative and nonconatenative patterns (e.g., Kannand Schütze, 2017; Nelson et al., 2020). In this paper, we verify that encoder-decoder networks can learn and generalize attested types of infixation and reduplication from modest training sets. We show further that the same networks readily learn many infixation and reduplication patterns that are unattested in natural languages, raising questions about their relationship to linguistic theory and viability as models of human learning.