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Abstract The conduction efficiency of ions in excitable tissues and of charged species in organic conjugated materials both benefit from having ordered domains and anisotropic pathways. In this study, a photocurrent‐generating cardiac biointerface is presented, particularly for investigating the sensitivity of cardiomyocytes to geometrically comply to biomacromolecular cues differentially assembled on a conductive nanogrooved substrate. Through a polymeric surface‐templated approach, photoconductive substrates with symmetric peptide‐quaterthiophene (4T)‐peptide units assembled as 1D nanostructures on nanoimprinted polyalkylthiophene (P3HT) surface are developed. The 4T‐based peptides studied here can form 1D nanostructures on prepatterned polyalkylthiophene substrates, as directed by hydrogen bonding, aromatic interactions between 4T and P3HT, and physical confinement on the nanogrooves. It is observed that smaller 4T‐peptide units that can achieve a higher degree of assembly order within the polymeric templates serve as a more efficient driver of cardiac cytoskeletal anisotropy than merely presenting aligned ‐RGD bioadhesive epitopes on a nanotopographic surface. These results unravel some insights on how cardiomyocytes perceive submicrometer dimensionality, local molecular order, and characteristics of surface cues in their immediate environment. Overall, the work offers a cardiac patterning platform that presents the possibility of a gene modification‐free cardiac photostimulation approach while controlling the conduction directionality of the biotic and abiotic components. 

Shapley value provides a unique way to fairly assess each player's contribution in a coalition and has enjoyed many applications. However, the exact computation of Shapley value is #P-hard due to the combinatoric nature of Shapley value. Many existing applications of Shapley value are based on Monte-Carlo approximation, which requires a large number of samples and the assessment of utility on many coalitions to reach high quality approximation, and thus is still far from being efficient. Can we achieve an efficient approximation of Shapley value by smartly obtaining samples? In this paper, we treat the sampling approach to Shapley value approximation as a stratified sampling problem. Our main technical contributions are a novel stratification design and two sample allocation methods based on Neyman allocation and empirical Bernstein bound, respectively. Experimental results on several real data sets and synthetic data sets demonstrate the effectiveness and efficiency of our novel stratification design and sampling approaches. 

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. 

Molecular doping can increase the conductivity of organic semiconductors and plays an increasingly important role in emerging and established plastic electronics applications. 4-(1,3-Dimethyl-2,3-dihydro-1 H -benzimidazol-2-yl)- N , N -dimethylaniline (N-DMBI-H) and tris(pentafluorophenyl)borane (BCF) are established n- and p-dopants, respectively, but neither functions as a simple one-electron redox agent. Molecular hydrogen has been suggested to be a byproduct in several proposed mechanisms for doping using both N-DMBI-H and BCF. In this paper we show for the first time the direct detection of molecular hydrogen in the uncatalysed doping of a variety of polymeric and molecular semiconductors using these dopants. Our results provide insight into the doping mechanism, providing information complementary to that obtained from more commonly applied methods such as optical, electron spin resonance, and electrical measurements.