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Abstract Electromagnetic hyperbolicity has driven key functionalities in nanophotonics, including super-resolution imaging, efficient energy control, and extreme light manipulation. Central to these advances are hyperbolic polaritons—nanometer-scale light-matter waves—spanning multiple energy-momentum dispersion orders with distinct mode profiles and incrementally high optical momenta. In this work, we report the mode conversion of hyperbolic polaritons across different dispersion orders by breaking the structure symmetry in engineered step-shape van der Waals (vdW) terraces. The mode conversion from the fundamental to high-order hyperbolic polaritons is imaged using scattering-type scanning near-field optical microscopy (s-SNOM) on both hexagonal boron nitride (hBN) and alpha-phase molybdenum trioxide (α-MoO₃) vdW terraces. Our s-SNOM data, augmented with electromagnetics simulations, further demonstrate the alteration of polariton mode conversion by varying the step size of vdW terraces. The mode conversion reported here offers a practical approach toward integrating previously independent different-order hyperbolic polaritons with ultra-high momenta, paving the way for promising applications in nano-optical circuits, sensing, computation, information processing, and super-resolution imaging. 

Efficient single instance segmentation is critical for unlocking features in on-the-fly mobile imaging applications, such as photo capture and editing. Existing mobile solutions often restrict segmentation to portraits or salient objects due to computational constraints. Recent advancements like the Segment Anything Model improve accuracy but remain computationally expensive for mobile, because it processes the entire image with heavy transformer backbones. To address this, we propose TraceNet, a one-click-driven single instance segmentation model. TraceNet segments a user-specified instance by back-tracing the receptive field of a ConvNet backbone, focusing computations on relevant regions and reducing inference cost and memory usage during mobile inference. Starting from user needs in real mobile applications, we define efficient single-instance segmentation tasks and introduce two novel metrics to evaluate both accuracy and robustness to low-quality input clicks. Extensive evaluations on the MS-COCO and LVIS datasets highlight TraceNet’s ability to generate high-quality instance masks efficiently and accurately while demonstrating robustness to imperfect user inputs.