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Longitudinal studies are often subject to missing data. The recent guidance from regulatory agencies, such as the ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classical control-based scenario for the treatment effect evaluation, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves n1/2-consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a rate not slower than n−1/4 when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulation studies and an antidepressant clinical trial.

 

Soft machines will require soft materials that exhibit a rich diversity of functionality, including shape morphing and photoresponsivity. The combination of these functionalities enables useful behaviors in soft machines that can be further developed by synthesizing materials that exhibit localized responsivity. Localized responsivity of liquid crystal elastomers (LCEs), which are soft materials that exhibit shape morphing, can be enabled by formulating composite inks for direct ink writing (DIW). Gold nanorods (AuNRs) can be added to LCEs to enable photothermal shape change upon absorption of light through a localized surface plasmon resonance. We compared LCE formulations, focusing on their amenability for printing by DIW and the photoresponsivity of AuNRs. The local responsivity of different three-dimensional architectures enabled soft machines that could oscillate, crawl, roll, transport mass, and display other unique modes of actuation and motion in response to light, making these promising functional materials for advanced applications. 

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.

 

Calibration weighting has been widely used to correct selection biases in nonprobability sampling, missing data and causal inference. The main idea is to calibrate the biased sample to the benchmark by adjusting the subject weights. However, hard calibration can produce enormous weights when an exact calibration is enforced on a large set of extraneous covariates. This article proposes a soft calibration scheme, where the outcome and the selection indicator follow mixed-effect models. The scheme imposes an exact calibration on the fixed effects and an approximate calibration on the random effects. On the one hand, our soft calibration has an intrinsic connection with best linear unbiased prediction, which results in a more efficient estimation compared to hard calibration. On the other hand, soft calibration weighting estimation can be envisioned as penalized propensity score weight estimation, with the penalty term motivated by the mixed-effect structure. The asymptotic distribution and a valid variance estimator are derived for soft calibration. We demonstrate the superiority of the proposed estimator over other competitors in simulation studies and using a real-world data application on the effect of BMI screening on childhood obesity.

 

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.