Dynamic hierarchical mimicking
WebAn active surface with an on-demand tunable topography holds great potential for various applications, such as reconfigurable metasurfaces, adaptive microlenses, soft robots and four-dimensional (4D) printing. Despite extensive progress, to achieve refined control of microscale surface structures with large-amplitude deformation remains a challenge. … WebSep 24, 2024 · Here we report a fibrous supramolecular network that can mimic nearly all of the aspects of collagen from dynamic hierarchical architecture to nonlinear mechanical behavior. This complex self-assembly system is solely based on a glucose polymer: curdlan, which is synthesized by bacteria and can form a similar triple helix as collagen.
Dynamic hierarchical mimicking
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WebNov 21, 2024 · [19] Duo Li and Qifeng Chen, “Dynamic hierarchical mimicking towards consistent optimization objectives, ” in Proceedings of the IEEE/CVF Conference on Computer V ision and Pattern Recognition ... WebMar 24, 2024 · Request PDF Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives While the depth of modern Convolutional Neural Networks …
WebJun 1, 2024 · Request PDF On Jun 1, 2024, Duo Li and others published Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives Find, read and … WebDynamic Treatment Recommendation (DTR) is a sequence of tailored treatment decision rules which can be grouped as individual sub-tasks. As the reward signals in DTR are hard to design, Imitation Learning (IL) has achieved great success as it is effective in mimicking doctors' behaviors from their demonstrations without explicit reward signals.
WebMar 18, 2015 · We used PEG polymers (M. W. 8000) as the crowding agents to mimic the cytoplasmic soup in a cell. Addition of crowding agents to long actin filaments resulted in an interesting hierarchical assembly with intriguing steps, sketched in Fig. 7a and shown as time-lapse images in Fig. 7b. Upon addition of PEG, actin filaments clustered at certain ... WebComplementary to previous training strategies, we propose Dynamic Hierarchical Mimicking, a generic feature learning mechanism, to advance CNN training with enhanced generalization ability. Partially inspired by DSN, we fork delicately designed side branches from the intermediate layers of a given neural network. Each branch can emerge from ...
Webposed Dynamic Hierarchical Mimicking, the training accu-racy curve tends to be lower than both the plain one and Deeply Supervised Learning, but our methodology leads to …
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