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Zero knowledge Neural Networks draw increasing attention for guaranteeing computation integrity and privacy of neural networks (NNs) based on zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) security scheme. However, the performance of zkSNARK NNs is far from optimal due to the million-scale circuit computation with heavy scalar-level dependency. In this paper, we propose a type-based optimizing framework for efficient zero-knowledge NN inference, namely ZENO (ZEro knowledge Neural network Optimizer). We first introduce ZENO language construct to maintain high-level semantics and the type information (e.g., privacy and tensor) for allowing more aggressive optimizations. We then propose privacytype driven and tensor-type driven optimizations to further optimize the generated zkSNARK circuit. Finally, we design a set of NN-centric system optimizations to further accelerate zkSNARK NNs. Experimental results show that ZENO achieves up to 8.5× end-to-end speedup than state-of-the-art zkSNARK NNs. We reduce proof time for VGG16 from 6 minutes to 48 seconds, which makes zkSNARK NNs practical. 

Connecting pre-bent liquid crystal elastomer fibers into a loop generates a self-regulated synchronized motion with snap through. 

The giant circular photo‐galvanic effect is realized in chiral metals when illuminated by circularly polarized light. However, the structure itself is not switchable nor is the crystal chirality in the adjacent chiral domains. Here spindle‐shaped liquid crystalline elastomer microparticles that can switch from prolate to spherical to oblate reversibly upon heating above the nematic to isotropic transition temperature are synthesized. When arranged in a honeycomb lattice, the continuous shape change of the microparticles leads to lattice reconfiguration, from a right‐handed chiral state to an achiral one, then to a left‐handed chiral state, without breaking the translational symmetry. Accordingly, the sign of rotation of the polarized light passing through the lattices changes as measured by time‐domain terahertz spectroscopy. Further, it can locally alter the chirality in the adjacent domains using near‐infrared light illumination. The reconfigurable chiral microarrays will allow us to explore non‐trivial symmetry‐protected transport modes of topological lattices at the light–matter interface. Specifically, the ability to controllably create chiral states at the boundary of the achiral/chiral domains will lead to rich structures emerging from the interplay of symmetry and topology.

 

We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data. We characterise the asymptotic distribution of the test-based estimator under local alternatives. We provide a data-adaptive procedure to select the test threshold that promises the smallest mean square error and an elastic confidence interval with a good finite-sample coverage property.

 

A liquid crystalline elastomer (LCE) network consisting of dynamic covalent bonds (DCBs) is referred as a LCE vitrimer. The mesogen alignment and the network topology can be reprogrammed locally in the LCE vitrimer by activating the bond exchange reactions using an external stimulus. After removal of the external stress, a new network is formed and the reprogrammed shape can be fixed, leading to a different set of the physical properties of the LCE vitrimers. Herein, this type of emerging materials is reviewed by a brief introduction of the fundamentals of LCEs, followed by discussions of various DCBs and the design principles for LCE vitrimers. After a presentation of different strategies to improve the stability and reprogrammability of the registered mesogen alignment, approaches to prepare LCE vitrimers with complex shapes and their actuations are discussed. Potential applications such as self‐healing and recycling, mechanochromic effects, and post‐functionalization of nanopores are also reviewed, followed by the conclusion of the remaining challenges and opportunities.

 

Abstract Liquid crystal elastomers that offer exceptional load-deformation response at low frequencies often require consideration of the mechanical anisotropy only along the two symmetry directions. However, emerging applications operating at high frequencies require all five true elastic constants. Here, we utilize Brillouin light spectroscopy to obtain the engineering moduli and probe the strain dependence of the elasticity anisotropy at gigahertz frequencies. The Young’s modulus anisotropy, E || / E ⊥ ~2.6, is unexpectedly lower than that measured by tensile testing, suggesting disparity between the local mesogenic orientation and the larger scale orientation of the network strands. Unprecedented is the robustness of E || / E ⊥ to uniaxial load that it does not comply with continuously transformable director orientation observed in the tensile testing. Likewise, the heat conductivity is directional, κ || / κ ⊥ ~3.0 with κ ⊥ = 0.16 Wm −1 K −1 . Conceptually, this work reveals the different length scales involved in the thermoelastic anisotropy and provides insights for programming liquid crystal elastomers on-demand for high-frequency applications.