Optical illusion has always attracted extensive attention, as it provides a superior self‐protection ability for both natural animals and human beings. A decade ago, this motivated the study and application of transformation optics, which provides a universal tool to manipulate light for invisibility cloaking and optical illusion. However, mainstream transformation‐optics‐based optical illusions are inherently hindered by the extreme requirements of metamaterial compositions in practice and unfavorably limited by the very large computational cost caused by their bulky state. To overcome these grand challenges, a novel and intelligent optical illusion supported by form‐free metasurfaces via a deep learning architecture is reported, which can not only render a similar illusion effect but also greatly reduces the parameter space in physics. Illustrative examples of conformal metasurfaces are presented, with a high‐fidelity inverse design from either the near‐ or far‐field in the simulation and experiment. Furthermore, a full set of intelligent systems is developed to benchmark the real‐world optical illusion applicability. The work brings the available illusion strategies closer to a wide range of in situ practical‐oriented applications and lays a foundation for the next generation of intelligent metamaterials.

Breakthroughs in the field of object recognition facilitate ubiquitous applications in the modern world, ranging from security and surveillance equipment to accessibility devices for the visually impaired. Recently-emerged optical computing provides a fundamentally new computing modality to accelerate its solution with photons; however, it still necessitates digital processing for in situ application, inextricably tied to Moore’s law. Here, from an entirely optical perspective, we introduce the concept of neuro-metamaterials that can be applied to realize a dynamic object- recognition system. The neuro-metamaterials are fabricated from inhomogeneous metamaterials or transmission metasurfaces, and optimized using, such as topology optimization and deep learning. We demonstrate the concept in experiments where living rabbits play freely in front of the neuro-metamaterials, which enable to perceive in light speed the rabbits’ representative postures. Furthermore, we show how this capability enables a new physical mechanism for creating dynamic optical mirages, through which a sequence of rabbit movements is converted into a holographic video of a different animal. Our work provides deep insight into how metamaterials could facilitate a myriad of in situ applications, such as illusive cloaking and speed-of-light information display, processing, and encryption, possibly ushering in an “Optical Internet of Things” era.

The physical basis of a smart city, the wireless channel, plays an important role in coordinating functions across a variety of systems and disordered environments, with numerous applications in wireless communication. However, conventional wireless channel typically necessitates high-complexity and energy-consuming hardware, and it is hindered by lengthy and iterative optimization strategies. Here, we introduce the concept of homeostatic neuro-metasurfaces to automatically and monolithically manage wireless channel in dynamics. These neuro-metasurfaces relieve the heavy reliance on traditional radio frequency components and embrace two iconic traits: They require no iterative computation and no human participation. In doing so, we develop a flexible deep learning paradigm for the global inverse design of large-scale metasurfaces, reaching an accuracy greater than 90%. In a full perception-decision-action experiment, our concept is demonstrated through a preliminary proof-of-concept verification and an on-demand wireless channel management. Our work provides a key advance for the next generation of electromagnetic smart cities.

Obtaining a full view and complete information of the surrounding dynamics is of great significance for a plethora of applications in sensing, imaging, navigation, and orientation. However, conventional spatial spectrum methods heavily rely on a priori knowledge with a trial‐and‐error solution fashion, leading to a great challenge to estimate complete information in volatile scenarios. Inspired by the mechanism of the jumping spider (Salticidae), here a universal detection approach driven by an intelligent antenna array, with the usage of amplitude‐only information as inputs, is introduced. The applied machine learning method can process the received time‐varying signals in one single feed‐forward computation, bypassing a heavy recline on prior knowledge of the array structure. As a demonstration, a compact eight‐port antenna array is designed for simultaneous attainments of frequency, direction of arrival, and polarization, covering the entire microwave X band. Both the simulated and experimental results show that the average accuracies for the azimuth angle, elevation angle, and polarization are up to 98%, with a millisecond detection time. Different from conventional methods, the strategy herein does not involve a complex beamforming network and a time‐consuming trial‐and‐error solution fashion, allowing a big step toward a miniaturized, integrated, and cost‐effective detector.

B7-H3, a new member of the B7 superfamily, plays a key role in the regulation of T cell-mediated immune responses. Our previous work showed that B7-H3 strongly augmented both LPS- and bacterial lipoprotein-induced NF-κB activation and inflammatory response, and soluble B7-H3 was elevated in CSF and plasma of patients with bacterial meningitis. MMP-9 has been implicated in blood-brain barrier disruption, inflammation, and vasculitis during the pathogenesis of bacterial meningitis. In this study, we report that in a murine model of pneumococcal meningitis, B7-H3 treatment enhances inflammatory response in the meninges, upregulates MMP-9 expression in cerebral parenchyma, and deteriorates clinical disease status indicated by weight loss and impaired movement ability. In vitro results showed that B7-H3 augmented MMP-9 secretion from Streptococcus pneumoniae-stimulated microglia cells. Thus, our data indicate that B7-H3 contributes to the development of pneumococcal meningitis by exaggerating inflammatory responses and upregulating MMP-9 activity in CNS, which ultimately lead to neuronal injury.

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Polarization, as a vector nature of the electromagnetic wave, plays a fundamental role in optics. Determining the polarization state of light is required by many applications, spanning from remote sensing and material analysis to biology and microscopy. To achieve this goal, conventional methods necessitate cascading of multiple optical components and consequential measurements to estimate the Stokes parameters, rendering the entire optical system bulky, complex, and sensitive. Here a brand-new strategy is introduced for direct polarization readout based on dual-channel neuro-metasurfaces. Neuro-metasurfaces can independently manipulate two orthogonal linearly-polarized waves that can synthesize arbitrary polarization waves with a linear combination. By judiciously designing the output focus points, a unique polarization atlas is created that allows one-to-one correspondence from intensity ratio to polarization state. To implement this, polarization-sensitive metasurfaces are designed and the spatial layout is optimized using a diffractive neural network. The feasibility of this strategy is validated by numerical simulation and microwave experiments. These results pave a new avenue in realizing integrated and multifunctional detectors and demonstrate the potential of neuro-metasurfaces as an add-on for discomposing and composing spatial basis.

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