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Artificial neural networks have revolutionized fields from computer vision to natural language processing, yet their growing energy and computational demands threaten future progress. Optical neural networks promise greater speed, bandwidth, and energy efficiency but suffer from weak optical nonlinearities. Here, we demonstrate a low-power, incoherent-light-compatible optical extreme learner that leverages “data nonlinearity” from optical pattern reverberations, eliminating reliance on intrinsic nonlinear materials. By encoding input data in the spatial polarization distribution of a tailored optical cavity and allowing light to pass through it multiple times, we achieve nonlinear transformations at extremely low optical power. Coupled with a simple trainable readout, our optical learner consistently outperforms linear digital networks in standard image classification tasks and XOR benchmarks, delivering accuracy matching fully nonlinear digital models. Our compact, energy-efficient approach substantially reduces complexity, cost, and energy consumption, paving the way for practical, scalable all-optical machine learning platforms. 

Light fields carry a wealth of information, including intensity, spectrum, and polarization. However, standard cameras capture only the intensity, disregarding other valuable information. While hyperspectral and polarimetric imaging systems capture spectral and polarization information, respectively, in addition to intensity, they are often bulky, slow, and costly. Here, we have developed an encoding metasurface paired with a neural network enabling a normal camera to acquire hyperspectro-polarimetric images from a single snapshot. Our experimental results demonstrate that this metasurface-enhanced camera can accurately resolve full-Stokes polarization across a broad spectral range (700 to 1150 nanometer) from a single snapshot, achieving a spectral sensitivity as high as 0.23 nanometer. In addition, our system captures full-Stokes hyperspectro-polarimetric video in real time at a rate of 28 frames per second, primarily limited by the camera’s readout rate. Our encoding metasurface offers a compact, fast, and cost-effective solution for multidimensional imaging that effectively uses information within light fields. 

We present broadband full-stokes spectropolarimetry using a chiral metasurface, which can retrieve spatially distributed wavelength information and full-stokes polarization states in a single shot. 

Sexual reproduction in angiosperms relies on precise communications between the pollen and pistil. The molecular mechanisms underlying these communications remain elusive. We established that in Arabidopsis , a stigmatic gatekeeper, the ANJEA–FERONIA (ANJ–FER) receptor kinase complex, perceives the RAPID ALKALINIZATION FACTOR peptides RALF23 and RALF33 to induce reactive oxygen species (ROS) production in the stigma papillae, whereas pollination reduces stigmatic ROS, allowing pollen hydration. Upon pollination, the POLLEN COAT PROTEIN B-class peptides (PCP-Bs) compete with RALF23/33 for binding to the ANJ–FER complex, leading to a decline of stigmatic ROS that facilitates pollen hydration. Our results elucidate a molecular gating mechanism in which distinct peptide classes from pollen compete with stigma peptides for interaction with a stigmatic receptor kinase complex, allowing the pollen to hydrate and germinate.