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Consider public health officials aiming to spread awareness about a new vaccine in a community interconnected by a social network. How can they distribute information with minimal resources, so as to avoid polarization and ensure community-wide convergence of opinion? To tackle such challenges, we initiate the study of sample complexity of opinion formation in networks. Our framework is built on the recognized opinion formation game, where we regard each agent’s opinion as a data-derived model, unlike previous works that treat opinions as data-independent scalars. The opinion model for every agent is initially learned from its local samples and evolves game-theoretically as all agents communicate with neighbors and revise their models towards an equilibrium. Our focus is on the sample complexity needed to ensure that the opinions converge to an equilibrium such that every agent’s final model has low generalization error. Our paper has two main technical results. First, we present a novel polynomial time optimization framework to quantify the total sample complexity for arbitrary networks, when the underlying learning problem is (generalized) linear regression. Second, we leverage this optimization to study the network gain which measures the improvement of sample complexity when learning over a network compared to that in isolation. Towards this end, we derive network gain bounds for various network classes including cliques, star graphs, and random regular graphs. Additionally, our framework provides a method to study sample distribution within the network, suggesting that it is sufficient to allocate samples inversely to the degree. Empirical results on both synthetic and real-world networks strongly support our theoretical findings. 

Morphing structures are often engineered with stresses introduced into a flat sheet by leveraging structural anisotropy or compositional heterogeneity. Here, we identify a simple and universal diffusion-based mechanism to enable a transient morphing effect in structures with parametric surface grooves, which can be realized with a single material and fabricated using low-cost manufacturing methods (e.g., stamping, molding, and casting). We demonstrate from quantitative experiments and multiphysics simulations that parametric surface grooving can induce temporary asynchronous swelling or deswelling and can transform flat objects into designed, three-dimensional shapes. By tuning the grooving pattern, we can achieve both zero (e.g., helices) and nonzero (e.g., saddles) Gaussian curvature geometries. This mechanism allows us to demonstrate approaches that could improve the efficiency of certain food manufacturing processes and facilitate the sustainable packaging of food, for instance, by creating morphing pasta that can be flat-packed to reduce the air space in the packaging.