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We present a novel prompting strategy for artificial intelligence driven digital avatars. To better quantify how our prompting strategy affects anthropomorphic features like humor, authenticity, and favorability we present Crowd Vote - an adaptation of Crowd Score that allows for judges to elect a large language model (LLM) candidate over competitors answering the same or similar prompts. To visualize the responses of our LLM, and the effectiveness of our prompting strategy we propose an end-to-end framework for creating high-fidelity artificial intelligence (AI) driven digital avatars. This pipeline effectively captures an individual's essence for interaction and our streaming algorithm delivers a high-quality digital avatar with real-time audio-video streaming from server to mobile device. Both our visualization tool, and our Crowd Vote metrics demonstrate our AI driven digital avatars have state-of-the-art humor, authenticity, and favorability outperforming all competitors and baselines. In the case of our Donald Trump and Joe Biden avatars, their authenticity and favorability are rated higher than even their real-world equivalents. 

Coarse-grained materials are widely accepted to display the highest strain hardening and the best tensile ductility. We experimentally report an attractive strain hardening rate throughout the deformation stage at 77 kelvin in a stable single-phase alloy with gradient dislocation cells that even surpasses its coarse-grained counterparts. Contrary to conventional understanding, the exceptional strain hardening arises from a distinctive dynamic structural refinement mechanism facilitated by the emission and motion of massive multiorientational tiny stacking faults (planar defects), which are fundamentally distinct from the traditional linear dislocation–mediated deformation. The dominance of atomic-scale planar deformation faulting in plastic deformation introduces a different approach for strengthening and hardening metallic materials, offering promising properties and potential applications. 

Gradient structures exist ubiquitously in nature and are increasingly being introduced in engineering. However, understanding structural gradient–related mechanical behaviors in all gradient structures, including those in engineering materials, has been challenging. We explored the mechanical performance of a gradient nanotwinned structure with highly tunable structural gradients in pure copper. A large structural gradient allows for superior work hardening and strength that can exceed those of the strongest component of the gradient structure. We found through systematic experiments and atomistic simulations that this unusual behavior is afforded by a unique patterning of ultrahigh densities of dislocations in the grain interiors. These observations not only shed light on gradient structures, but may also indicate a promising route for improving the mechanical properties of materials through gradient design.