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Quantum ghost imaging (QGI) leverages correlations between entangled photon pairs to reconstruct an image using light that has never physically interacted with an object. Despite extensive research interest, this technique has long been hindered by slow acquisition speeds, due to the use of raster-scanned detectors or the slow response of intensified cameras. Here, we utilize a single-photon-sensitive time-stamping camera to perform QGI at ultra-low-light levels with rapid data acquisition and processing times, achieving high-resolution and high-contrast images in under 1 min. Our work addresses the trade-off between image quality, optical power, data acquisition time, and data processing time in QGI, paving the way for practical applications in biomedical and quantum-secured imaging. 

Abstract Nature provides many examples of the benefits of nanoscopic surface structures in areas of adhesion and antifouling. Herein, the design, fabrication, and characterization of liquid crystal elastomer (LCE) films are presented with nanowire surface structures that exhibit tunable stimuli‐responsive deformations and enhanced adhesion properties. The LCE films are shown to curl toward the side with the nanowires when stimulated by heat or organic solvent vapors. In contrast, when a droplet of the same solvent is placed on the film, it curls away from the nanowire side due to nanowire‐induced capillary forces that cause unequal swelling. This characteristic curling deformation is shown to be reversible and can be optimized to match curved substrates, maximizing adhesive shear forces. By using chemical modification, the LCE nanowire films can be given underwater superoleophobicity, enabling oil repellency under a range of harsh conditions. This is combined with the nanowire‐induced frictional asymmetry and the reversible shape deformation to create an underwater droplet mixing robot, capable of performing chemical reactions in aqueous environments. These findings demonstrate the potential of nanowire‐augmented LCE films for advanced applications in soft robotics, adaptive adhesion, and easy chemical modification, with implications for designing responsive materials that integrate mechanical flexibility with surface functionality. 

According to the Entropy Rate Constancy (ERC) principle, the information density of a text is approximately constant over its length. Whether this principle also applies to nonverbal commu- nication signals is still under investigation. We perform empirical analyses of video-recorded dialogue data and investigate whether listener gaze, as an important nonverbal communication signal, adheres to the ERC principle. Results show (1) that the ERC principle holds for lis- tener gaze; and (2) that the two linguistic factors syntactic complexity and turn transition poten- tial are weakly correlated with local entropy of listener gaze. 

We present a generalization of the geometric phase to pure and thermal states in $$\mathcal{PT}$$-symmetric quantum mechanics (PTQM) based on the approach of the interferometric geometric phase (IGP). The formalism first introduces the parallel-transport conditions of quantum states and reveals two geometric phases, $$\theta^1$$ and $$\theta^2$$, for pure states in PTQM according to the states under parallel-transport. Due to the non-Hermitian Hamiltonian in PTQM, $$\theta^1$$ is complex and $$\theta^2$$ is its real part. The imaginary part of $$\theta^1$$ plays an important role when we generalize the IGP to thermal states in PTQM. The generalized IGP modifies the thermal distribution of a thermal state, thereby introducing effective temperatures. \textcolor{red}{At certain critical points, the generalized IGP may exhibit discrete jumps at finite temperatures, signaling a geometric phase transition. We illustrate the IGP of PTQM through two examples and compare their differences}. 

Abstract Marine microplastics are emerging as a growing environmental concern due to their potential harm to marine biota. The substantial variations in their physical and chemical properties pose a significant challenge when it comes to sampling and characterizing small-sized microplastics. In this study, we introduce a novel microfluidic approach that simplifies the trapping and identification process of microplastics in surface seawater, eliminating the need for labeling. We examine various models, including support vector machine, random forest, convolutional neural network (CNN), and residual neural network (ResNet34), to assess their performance in identifying 11 common plastics. Our findings reveal that the CNN method outperforms the other models, achieving an impressive accuracy of 93% and a mean area under the curve of 98 ± 0.02%. Furthermore, we demonstrate that miniaturized devices can effectively trap and identify microplastics smaller than 50 µm. Overall, this proposed approach facilitates efficient sampling and identification of small-sized microplastics, potentially contributing to crucial long-term monitoring and treatment efforts.