arXiv subjects

Haoyi Yu

Publications and source records attributed to Haoyi Yu.

9 recordsLinked to original sources

arXiv2026

Quantum skyrmion parallelism via metasurface-tailored high-dimensional entanglement

Quantum skyrmions are topological structures that have garnered significant interest due to their demonstrated robustness and versatility across diverse optical platforms. However, existing approaches for their generation are limited to producing pre-determined two dimensional qubit states with a single topology. Here we create multi-dimensional topological states by introducing a non-local interaction between high-dimensional photonic entanglement and a metasurface, where the topological transformation induced by the metasurface is made non-deterministic by the probabilistic nature of the interfacing entangled state. Within this framework, we demonstrate that individual quantum states can host multiple co-existing topologies that are only revealed upon measurement, allowing for their parallel transport within distinct spatial mode channels. We confirm this by revealing the rich topological landscape within our modified Hilbert space while controlling the desired output topology by orbital angular momentum (OAM) projections on one of the entangled photons, producing multiple non-local polarization-OAM entangled states characterized by distinct topological classes, all from a single metasurface device. Our results reveal new capability when structured high-dimensional entanglement is interfaced with structured matter capable of coupling photonic degrees of freedom, establishing a new pathway for the compact generation of complex quantum states.

quant-ph↗

arXiv2026

Metalens stereoscopic optical palpation for imaging cancer mechanics

Accurate intraoperative margin assessment is critical for complete resection of solid tumours. However, surgeons routinely rely on manual palpation, which is highly subjective and fundamentally limited by poor spatial resolution, and pre-operative imaging lacks real-time feedback. While emerging optical elastography techniques offer quantitative mechanical contrast, their reliance on bulky optical components necessitates cumbersome alignment and hinders integration into miniaturised surgical instruments. Here, we report metalens stereoscopic optical palpation (MSOP), a nanophotonic imaging platform leveraging a binocular metalens integrated with a single CMOS sensor to achieve high-fidelity, self-aligned elastography. By replacing complex bulk optics with a compact stereoscopic architecture, MSOP enables robust, high-contrast mapping of tissue surface stress. Following validation in heterogeneous silicone phantoms, we demonstrate MSOP's translational potential by characterising the mechanical signatures of malignancies in fresh mouse pancreatic cancer models, as well as excised human breast and liver specimens. By providing reliable, label-free mechanical contrast across diverse oncological landscapes, MSOP offers a compact tool for precise intraoperative margin delineation to reduce re-excision rates and improve surgical outcomes.

physics.optics↗

arXiv2026

3DReflecNet: A Large-Scale Dataset for 3D Reconstruction of Reflective, Transparent, and Low-Texture Objects

Accurate 3D reconstruction of objects with reflective, transparent, or low-texture surfaces still remains notoriously challenging. Such materials often violate key assumptions in multi-view reconstruction pipelines, such as photometric consistency and the availability on distinct geometric texture cues. Existing datasets primarily focus on diffuse, textured objects, and therefore provide limited insight into performance under real-world material complexities. We introduce 3DReflecNet, a large-scale hybrid dataset exceeding 22 TB that is specifically designed to benchmark and advance 3D vision methods for these challenging materials. 3DReflecNet combines two types of data: over 120,000 synthetic instances generated via physically-based rendering of more than 12,000 shapes, and over 1,000 real-world objects captured using consumer devices. Together, these data consist of more than 7 million multi-view frames. The dataset spans diverse materials, complex lighting conditions, and a wide range of geometric forms, including shapes generated from both real and LLM-synthesized 2D images using diffusion-based pipelines. To support robust evaluation, we design benchmarks for five core tasks: image matching, structure-from-motion, novel view synthesis, reflection removal, and relighting. Extensive experiments demonstrate that state-of-the-art methods struggle to maintain accuracy across these settings, highlighting the need for more resilient 3D vision models.

cs.CV↗

arXiv2025

Demultiplexing through a multimode fiber using chip-scale diffractive neural networks

In today's information age, advanced fiber optic transmission technology is of paramount importance. Multimode fibers (MMFs) using space-division multiplexing (SDM) are promising for improved transmission capacity, connection flexibility, and security of data. However, the complex transmission characteristics of MMFs significantly hinder precise mode demultiplexing. Conventional approaches, including holographic measurements, phase retrieval algorithms, photonic lanterns, and multiplane light conversion, are limited by system complexity, size, and flexibility. In this paper, we demonstrate for the first time a purely optical, chip-scale AI solution for high-mode isolation, speed-of-light demultiplexing of MMF modes using a three-dimensional diffractive neural network (DNN). The DNN is trained with synthetic modal data and fabricated using two-photon nanolithography. It features a compact size of $120{\mu}m \times 120{\mu}m \times 80{\mu}m$ and a diffractive structure size of $1{\mu}m^{2}$ for the neurons at the hidden layers of the network. Experimentally, the DNN demultiplexer achieves a relative demultiplexing accuracy of over 80%. The AI approach of DNN allows for flexible design and overcomes the size and performance limitations of digital-optical demultiplexers. This work paves the way for compact, low-latency optical processors for high-performance demultiplexers and enables scalable, chip-integrated solutions for next-generation fiber optic networks.

physics.optics↗

arXiv2025

Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation

We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of billions to one trillion parameters under a unified Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity, cross-scale consistency, and efficiency guided by empirical scaling laws. The series includes three non-thinking (instruct) models - Ling-mini-2.0, Ling-flash-2.0, and Ling-1T - ranging from 16B to 1T total parameters and achieving up to 7-fold active-compute efficiency compared with dense counterparts. Ling 2.0 integrates coordinated innovations across model architecture, pre-training, post-training, and infrastructure: a high-sparsity MoE with MTP for efficient reasoning, reasoning-oriented data and mid-training CoT activation, reinforcement-based fine-tuning (DFT, Evo-CoT), and full-scale FP8 training with fine-grained heterogeneous pipelines. At the trillion scale, Ling-1T establishes a new Pareto frontier of reasoning accuracy versus computational efficiency, demonstrating that sparse activation, when properly aligned with reasoning objectives, enables scalable and efficient intelligence. Collectively, Ling 2.0 provides a coherent, open, and efficient foundation for advancing future reasoning and thinking models, including the Ring series built upon the same base.

cs.CL↗

arXiv2025

Disorder-enabled Synthetic Metasurfaces

Optical metasurfaces have catalyzed transformative advances across imaging, optoelectronics, quantum information processing, sensing, energy conversion, and optical computing. Yet, despite this rapid progress, most research remains focused on optimizing single functionalities, constrained by the persistent challenge of integrating multiple functions within a single device. Here, we demonstrate that engineered structural disorder of metapixels, used to implement a photonic function, can significantly reduce the area required across the entire aperture without compromising optical performance. The unallocated space can then be repurposed to encode functionally distinct metapixels without increasing the design complexity, each independently addressable via various optical degrees of freedom. As a proof of concept, we present a synthetic achromatic metalens featuring 11 spectrally distinct lens profiles encoded through nonlocal metapixels engineered to support sharp resonances via quasi bound states in the continuum. This large-scale metalens with 8.1 mm aperture achieves diffraction-limited achromatic focusing across the 1200 to 1400 nm spectral window. We further incorporate polarization-selective metapixels to implement momentum-space distinct gratings, enabling single-shot, high spatial resolution polarimetric imaging of arbitrarily structured light fields, including radial and azimuthal vector beams and optical skyrmions. Altogether, this disorder-enabled synthetic metasurface platform establishes a versatile foundation for unifying diverse photonic functionalities within a single optical element, marking a substantial step toward compact, high-density, multifunctional optical devices.

physics.optics↗

arXiv2025

Resonant helical dichroism in twisted dielectric metastructures

Circular dichroism, arising from interactions with light fields of opposite spin angular momentum, has become a fundamental tool for molecular characterization. Meanwhile, helical dichroism (HD) - the dichroic response to vortex beams carrying opposite orbital angular momentum (OAM) - offers an alternative approach for probing chiral molecules and photonic structures. Previous demonstrations of HD have been limited to non-resonant light-matter interactions with chiral micro- and nanostructures, leaving the realization of resonance helical dichroism largely unexplored. Here, we present the design and implementation of twisted dielectric metastructures, composed of an array of rotated silicon trimer nanostructures harnessing nonlocal photonic modes with a high quality factor of several dozen that enable strong resonant HD for OAM values up to $10$. We experimentally demonstrate resonantly enhanced HD for strongly focused OAM beams with the magnitude of topological charges from $1$ to $3$. Our findings pave the way for resonant nanophotonics involving OAM beams, unlocking the full potential of structured light for applications in molecular sensing, optical imaging, nonlinear optics, and optical data storage.

physics.optics↗

arXiv2024

Ultrathin BIC metasurfaces based on ultralow-loss Sb2Se3 phase-change material

Phase-change materials (PCMs) are increasingly recognised as promising platforms for tunable photonic devices due to their ability to modulate optical properties through solid-state phase transitions. Ultrathin and low-loss PCMs are highly valued for their fast and more effective phase transitions and applications in reconfigurable photonic chips, metasurfaces, optical modulators, sensors, photonic memories, and neuromorphic computing. However, conventional PCMs such as GST, GSST, VO2, and In3SbTe2, despite optimisation for tunable meta-optics, suffer from high intrinsic losses in the near-infrared (NIR) region, limiting their potential for high quality factor (Q-factor) resonant metasurfaces. Here we present the design and fabrication of tunable bound states in the continuum (BIC) metasurfaces using the ultralow-loss PCM Sb2Se3. Our BIC metasurfaces, only 25 nm thick, achieve high modulation depth and broad resonance tuning in the NIR with high Q-factors up to 130, without the need for additional materials. Experimentally, we employ these BIC metasurfaces to modulate photoluminescence in rare earth-doped upconversion nanoparticles, reducing the excitation power for multiphoton photoluminescence and enabling emission polarisation manipulation. This work offers a promising platform for developing active resonant metasurfaces in the NIR region, with broad applications including super resolution imaging, optical modulation, ultrafast switches, harmonic generation, colour filtering, and optical sensing.

physics.optics↗

arXiv2023

AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices

Mobile video applications today have attracted significant attention. Deep learning model (e.g. deep neural network, DNN) compression is widely used to enable on-device inference for facilitating robust and private mobile video applications. The compressed DNN, however, is vulnerable to the agnostic data drift of the live video captured from the dynamically changing mobile scenarios. To combat the data drift, mobile ends rely on edge servers to continuously evolve and re-compress the DNN with freshly collected data. We design a framework, AdaEvo, that efficiently supports the resource-limited edge server handling mobile DNN evolution tasks from multiple mobile ends. The key goal of AdaEvo is to maximize the average quality of experience (QoE), e.g. the proportion of high-quality DNN service time to the entire life cycle, for all mobile ends. Specifically, it estimates the DNN accuracy drops at the mobile end without labels and performs a dedicated video frame sampling strategy to control the size of retraining data. In addition, it balances the limited computing and memory resources on the edge server and the competition between asynchronous tasks initiated by different mobile users. With an extensive evaluation of real-world videos from mobile scenarios and across four diverse mobile tasks, experimental results show that AdaEvo enables up to 34% accuracy improvement and 32% average QoE improvement.

cs.DC↗