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Nat. Photonics2009, 3, 534540.(4) Yang, D.; Ye, S.; Ge, J. From Metastable Colloidal CrystallineArrays to Fast Responsive Mechanochromic Photonic Gels: AnOrganic Gel for Deformation-Based Display Panels. ... J.; Winkler, H.; Viel, B.; Spahn,P.; Ruhl, T.

objects found anywhere in Britain during this period: the first age of metal. ... The role is based at the Centre of Architecture, Urbanism, and Global Heritage.

Three-dimensional printing is a highly flexible prototyping. method and allowed many iterations of designs to be printedand tested before the final design was produced. ... Figure 5. A typical display produced by the online infrastructure, showing the

Neuronal mechanisms of utility-based choice may thus be 37 common to both risky and riskless choices. ... The different utility functions 45 should be taken into account in neuronal investigations of utility-based choice.

This opens up the potential for durable and energy-efficient yet reusable and reconfigurable displays, wearables, or packaging and security labeling based on such polymeric film materials. ... We first characterize the material’s mechanical properties

If it does both, the scalar arguments will be first, and the list argument will follow. ... Also, any file containing null in the first block is considered a binary file.

The NST was a flexible course, and I homed in on biological options. ... One of my first experiments from that period was never published except for being mentioned in chapters.

temporal difference learning models, is compatible with model-. free and model-based reinforcement learning, reports the. ... 11. Doya K, Samejima K, Katagiri K, Kawato M: Multiple model-based reinforcement learning.

contemporary forms and renewed popularity in the early twenty-first century. The fifteen. ... finest paper-hangings ever produced’, to the Victorian upper classes. He also praises the.

temporal difference learning models, is compatible with model-. free and model-based reinforcement learning, reports the. ... 11. Doya K, Samejima K, Katagiri K, Kawato M: Multiple model-based reinforcement learning.