arXiv subjects

Chao He

Publications and source records attributed to Chao He.

At least 37 records · Page 2Linked to original sources

arXiv2026

Skyrmions based on optical anisotropy for topological encoding

The observation of skyrmions across diverse physical domains suggests that they are universal features of S$^{2}$-valued fields, reflecting the ubiquity of topology in the study of the natural world. In this paper, we develop an abstract technique of parameter space dimensionality reduction that extends the skyrmion framework to fields taking values in manifolds of dimension greater than 2, thereby broadening the range of systems that can support skyrmions. To prove that this is more than just a mathematical abstraction, we apply our technique to light-matter interactions, directly encoding skyrmionic structures into the optical anisotropy of spatially varying structured matter by selecting a distinguished axis, an approach fundamentally different from the more commonly known skyrmions formed by director fields in liquid crystals. We experimentally realize such skyrmions using a liquid-crystal-based tunable elliptical retarder array as a proof-of-concept platform and demonstrate complex, reconfigurable skyrmionic states exhibiting topological robustness under artificially introduced stochastic perturbations. Exploiting this robustness, we demonstrate a promising application of skyrmions in topologically protected information storage and provide an easily verifiable rule, which we term the 60° rule, that serves as a practical engineering criterion for guaranteeing robustness against noise and measurement errors.

physics.optics↗

arXiv2026

GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights

Glomerular pathology is central to the diagnosis and prognosis of renal diseases, yet the heterogeneity of glomerular morphology and fine-grained lesion patterns remain challenging for current AI approaches. We present GloPath, an entity-centric foundation model trained on over one million glomeruli extracted from 14,049 renal biopsy specimens using multi-scale and multi-view self-supervised learning. GloPath addresses two major challenges in nephropathology: glomerular lesion assessment and clinicopathological insights discovery. For lesion assessment, GloPath was benchmarked across three independent cohorts on 52 tasks, including lesion recognition, grading, few-shot classification, and cross-modality diagnosis-outperforming state-of-the-art methods in 42 tasks (80.8%). In the large-scale real-world study, it achieved an ROC-AUC of 91.51% for lesion recognition, demonstrating strong robustness in routine clinical settings. For clinicopathological insights, GloPath systematically revealed statistically significant associations between glomerular morphological parameters and clinical indicators across 224 morphology-clinical variable pairs, demonstrating its capacity to connect tissue-level pathology with patient-level outcomes. Together, these results position GloPath as a scalable and interpretable platform for glomerular lesion assessment and clinicopathological discovery, representing a step toward clinically translatable AI in renal pathology.

cs.CV↗

arXiv2026

The Role of Hydrogen and Oxygen Interstitial Defects in Crystalline Si cells: Mechanism of Device Degradation in Humid Environment

The efficiency of silicon solar cells gradually decreases in various environments, with humidity being a key factor contributing to this decline through moisture-induced degradation (MID) involving multiple mechanisms including encapsulant hydrolysis and metal ion migration. Among these mechanisms, the role of water-derived hydrogen and oxygen interstitial defects represents an underexplored yet fundamental degradation pathway. This study employs density functional theory and quantum transport theory to investigate hydrogen and oxygen interstitial defects as a novel perspective for understanding MID mechanisms. Results reveal that neutral hydrogen interstitials at bond-center sites exhibit low diffusion barriers (0.96 eV) and act as deep-level recombination centers, while oxygen interstitials face higher diffusion barriers (2.2 eV) with limited trapping capability. Device simulations demonstrate that hydrogen defects cause substantially more pronounced photovoltaic current degradation through enhanced non-radiative recombination. Critically, under humid conditions, hydrogen from water molecules readily penetrates silicon lattices forming active recombination centers, while oxygen incorporation remains kinetically limited with negligible impact. This interstitial defect perspective provides novel understanding of MID mechanisms, explaining why moisture exposure primarily degrades silicon solar cells through hydrogen rather than oxygen incorporation, offering fundamental insights for developing targeted mitigation strategies.

cond-mat.mtrl-sci↗

arXiv2026

Topologically robust programmable logic arrays using light and matter skyrmions

Photonic computing offers a low-power, high-bandwidth paradigm for information processing; however, the analogue nature of conventional architectures means that intrinsic noise and fabrication imperfections greatly impact performance, thereby severely limiting scalability. Recent work on optical skyrmions offers a route to overcoming these limitations by exploiting perturbation-resilient topological invariants assigned to the optical field for computation. Crucially, owing to its relative novelty, an architectural perspective on integrating individual components that manipulate topological charge into a functional system remains an important open goal. In this paper, we take concrete steps toward system-level design by introducing a platform-independent architecture for skyrmion-based logic, built around a modular library of topologically robust optical primitives, including generators, converters, registers, and adders. This framework enables the synthesis and arithmetic manipulation of topological numbers within a unified programmable architecture. We then experimentally validate this approach using multichannel arrays, demonstrating accurate charge readout and high robustness against alignment errors and environmental noise. These results provide a scalable foundation for topologically robust programmable logic arrays, paving the way for compact and integrated photonic processing circuits.

physics.optics↗

arXiv2026

General in situ feedback control of cascaded liquid crystal spatial light modulators for structured field generation

Cascaded liquid crystal spatial light modulators provide a versatile strategy for the generation of structured light and matter fields, with applications including optical communications, photonic computing, and topological field engineering. However, experimental imperfections, such as temperature-dependent liquid crystal response, variations between individual pixels, and alignment errors, present significant engineering challenges in generating high-quality fields. Moreover, changes in experimental conditions over time mean that calibrating each component once is insufficient for maintaining long-term, high-quality field generation. To address this, we present a general engineering approach based on a bespoke, physically informed, and manifold-constrained gradient-descent scheme that enables in situ feedback control, compensating for such errors in real time without the need to alter the experimental setup. We further demonstrate the correction efficacy of our proposed strategy through experiments in both spatially varying light and matter field generation, including scenarios in which complex vectorial aberrations are artificially introduced into the setup. Together, these demonstrations underscore the practicality of our method and its suitability for deployment in real-world experimental environments, paving the way for robust operation of cascaded architectures for structured field generation.

physics.optics↗

arXiv2026

Resolving topological obstructions to vectorial structured field control

The use of structured matter, such as optical retarders, for vectorial control is a well-established and widely employed technique in modern optics, and has driven continued advances in the manipulation of complex, spatially varying vectorial fields. However, achieving arbitrary field conversion typically requires the use of cascaded elements, as intrinsic physical and fabrication constraints fundamentally limit individual devices to a restricted subset of transformations. This results in an overall continuous transformation potentially failing to be continuous at the level of the parameters of the cascade, leading to detrimental engineering consequences such as the introduction of complex, discontinuous aberrations that disrupt important topological properties of the underlying matter field. In this work, we establish a novel mathematical framework for analyzing the topological difficulties that emerge in the decomposition of an overall transformation into individual layers, and for determining the minimal depth required to overcome them. The strategy introduced provides a general pathway for optimizing designs for vectorial field control and matter field generation, with particular significance for the manipulation of topological phases in optical polarization fields, such as Stokes skyrmions, where continuity is of vital importance.

physics.optics↗

arXiv2026

NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents

Recent advances in coding agents suggest rapid progress toward autonomous software development, yet existing benchmarks fail to rigorously evaluate the long-horizon capabilities required to build complete software systems. Most prior evaluations focus on localized code generation, scaffolded completion, or short-term repair tasks, leaving open the question of whether agents can sustain coherent reasoning, planning, and execution over the extended horizons demanded by real-world repository construction. To address this gap, we present NL2Repo Bench, a benchmark explicitly designed to evaluate the long-horizon repository generation ability of coding agents. Given only a single natural-language requirements document and an empty workspace, agents must autonomously design the architecture, manage dependencies, implement multi-module logic, and produce a fully installable Python library. Our experiments across state-of-the-art open- and closed-source models reveal that long-horizon repository generation remains largely unsolved: even the strongest agents achieve below 40% average test pass rates and rarely complete an entire repository correctly. Detailed analysis uncovers fundamental long-horizon failure modes, including premature termination, loss of global coherence, fragile cross-file dependencies, and inadequate planning over hundreds of interaction steps. NL2Repo Bench establishes a rigorous, verifiable testbed for measuring sustained agentic competence and highlights long-horizon reasoning as a central bottleneck for the next generation of autonomous coding agents.

cs.CL↗

arXiv2026

Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining

Immunohistochemical (IHC) staining provides crucial molecular characterization of tissue samples and plays an indispensable role in the clinical examination and diagnosis of cancers. However, compared with the commonly used Hematoxylin and Eosin (H&E) staining, IHC staining involves complex procedures and is both time-consuming and expensive, which limits its widespread clinical use. Virtual staining converts H&E images to IHC images, offering a cost-effective alternative to clinical IHC staining. Nevertheless, using adjacent slides as ground truth often results in weakly-paired data with spatial misalignment and local deformations, hindering effective supervised learning. To address these challenges, we propose a novel topology-aware framework for H&E-to-IHC virtual staining. Specifically, we introduce a Topology-aware Consistency Matching (TACM) mechanism that employs graph contrastive learning and topological perturbations to learn robust matching patterns despite spatial misalignments, ensuring structural consistency. Furthermore, we propose a Topology-constrained Pathological Matching (TCPM) mechanism that aligns pathological positive regions based on node importance to enhance pathological consistency. Extensive experiments on two benchmarks across four staining tasks demonstrate that our method outperforms state-of-the-art approaches, achieving superior generation quality with higher clinical relevance.

cs.CV↗

arXiv2025

Rethinking conditioning in polarimetry: a new framework beyond $\ell^2$-based metrics

A standard procedure to achieve accurate, precise, and fast polarization measurement is to choose analyzing and generating polarization states that yield an $\ell^2$-condition number optimized instrument matrix. This strategy works well for rotating-waveplate systems, where the accessible polarization states trace a curve on the Poincaré sphere and the corresponding optimization problem is generally well posed. However, it becomes degenerate for liquid-crystal-based systems, which can generate arbitrary polarization states, and whose additional degrees of freedom allow the optimization of metrics beyond the $\ell^2$-condition number. Leveraging this unique advantage of liquid-crystal polarimeters, we introduce additional performance measures derived from alternative norms and error distributions computed via Monte Carlo simulations to inform the design of measurement schemes. We then experimentally demonstrate their effectiveness in suppressing errors, paving the way for more robust and efficient polarization measurements.

physics.optics↗

arXiv2025

Multiplexed vector beam conversion via complex structured matter

Structured light, in which the amplitude, phase, and polarization of an optical field are deliberately tailored in space and time, has enabled unprecedented control over optical fields, paving the way for diverse applications across photonics and optical engineering. However, the prevailing design philosophy, which predominantly focuses on converting a single fixed input into a single desired output, relies on tunability to achieve time-division multiplexing rather than intrinsic design, and is fundamentally incompatible with wavelength-division multiplexing. Here, we propose a general framework for designing structured matter capable of achieving multiple input-output relations simultaneously, thereby enabling passive devices to realize both time-division and wavelength-division multiplexing. Using Stokes skyrmions, which have recently gained attention for their topological properties and promising applications in modern optical communication and computing, as an example, we demonstrate that a simple retarder-diattenuator-retarder cascade can be designed to simultaneously satisfy three arbitrary input-output relations, enabling diverse functionalities within a single passive element. This approach enables complex and multiplexed manipulation of topological numbers, paving the way for high-dimensional on-chip photonic computing based on optical skyrmions.

physics.optics↗

arXiv2025

Full vectorial field sensing using liquid crystal droplet arrays

Determining the amplitude, phase, and polarization profile of light is essential for both fundamental scientific discovery and applications spanning optical metrology, microscopy, astronomy, and optical communication/computing technologies. However, most modern measurement approaches are unable to retrieve such parameters readily, often relying on bulky and expensive hardware, or lacking the capability for single-shot sensing. Here, we introduce a low cost, compact, full vectorial field sensor based on an inkjet-printed nematic liquid crystal droplet array that enables simultaneous measurement of these important characteristics of light. Polarization and intensity are measured via division-of-wavefront polarimetry, exploiting the droplets' spatially varying birefringence, while the phase is reconstructed by treating each droplet as a separate microlens in a Shack-Hartmann-like wavefront sensor configuration. To demonstrate the system's performance, we characterize aberrated dual-wavelength beams carrying distinct intensity, phase, and polarization information, confirming accurate retrieval of the optical field profiles for both spectral components.

physics.optics↗

arXiv2025

Beyond H&E: Unlocking Pathological Insights with Polarization Imaging

Histopathology image analysis is fundamental to digital pathology, with hematoxylin and eosin (H&E) staining as the gold standard for diagnostic and prognostic assessments. While H&E imaging effectively highlights cellular and tissue structures, it lacks sensitivity to birefringence and tissue anisotropy, which are crucial for assessing collagen organization, fiber alignment, and microstructural alterations--key indicators of tumor progression, fibrosis, and other pathological conditions. To bridge this gap, we construct a polarization imaging system and curate a new dataset of over 13,000 paired Polar-H&E images. Visualizations of polarization properties reveal distinctive optical signatures in pathological tissues, underscoring its diagnostic value. Building on this dataset, we propose PolarHE, a dual-modality fusion framework that integrates H&E with polarization imaging, leveraging the latter ability to enhance tissue characterization. Our approach employs a feature decomposition strategy to disentangle common and modality specific features, ensuring effective multimodal representation learning. Through comprehensive validation, our approach significantly outperforms previous methods, achieving an accuracy of 86.70% on the Chaoyang dataset and 89.06% on the MHIST dataset. These results demonstrate that polarization imaging is a powerful and underutilized modality in computational pathology, enriching feature representation and improving diagnostic accuracy. PolarHE establishes a promising direction for multimodal learning, paving the way for more interpretable and generalizable pathology models.

eess.IV↗

arXiv2025

MSMT-FN: Multi-segment Multi-task Fusion Network for Marketing Audio Classification

Audio classification plays an essential role in sentiment analysis and emotion recognition, especially for analyzing customer attitudes in marketing phone calls. Efficiently categorizing customer purchasing propensity from large volumes of audio data remains challenging. In this work, we propose a novel Multi-Segment Multi-Task Fusion Network (MSMT-FN) that is uniquely designed for addressing this business demand. Evaluations conducted on our proprietary MarketCalls dataset, as well as established benchmarks (CMU-MOSI, CMU-MOSEI, and MELD), show MSMT-FN consistently outperforms or matches state-of-the-art methods. Additionally, our newly curated MarketCalls dataset will be available upon request, and the code base is made accessible at GitHub Repository MSMT-FN, to facilitate further research and advancements in audio classification domain.

cs.SD↗

arXiv2025

Perturbation-resilient integer arithmetic using optical skyrmions

The decline of Moore's law coupled with the rise of artificial intelligence has recently motivated research into photonic computing as a high-bandwidth, low-power strategy to accelerate digital electronics. However, many modern-day photonic computing strategies are analog, making them susceptible to noise and intrinsically difficult to scale. Optical skyrmions offer a route to overcoming these limitations through digitization in the form of a discrete topological number that can be assigned to the analog optical field. Apart from an intrinsic robustness against perturbations, optical skyrmions represent a new medium that has yet to be fully exploited for photonic computing, namely spatially varying polarization. Here, we propose and experimentally demonstrate a method for performing perturbation-resilient integer arithmetic with optical skyrmions and passive optical components. To the best of our knowledge, this is the first time such discrete mathematical operations have been directly achieved using optical skyrmions without external energy input.

physics.optics↗

arXiv2025

From Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology

Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise, these models still lack accuracy and generalizability. General foundation models offer a broader reach but remain limited in capturing subspecialty-specific features and task adaptability. We introduce the Cervical Subspecialty Pathology (CerS-Path) diagnostic system, developed through two synergistic pretraining stages: self-supervised learning on approximately 190 million tissue patches from 140,000 slides to build a cervical-specific feature extractor, and multimodal enhancement with 2.5 million image-text pairs, followed by integration with multiple downstream diagnostic functions. Supporting eight diagnostic functions, including rare cancer classification and multimodal Q&A, CerS-Path surpasses prior foundation models in scope and clinical applicability. Comprehensive evaluations demonstrate a significant advance in cervical pathology, with prospective testing on 3,173 cases across five centers maintaining 99.38% screening sensitivity and excellent generalizability, highlighting its potential for subspecialty diagnostic translation and cervical cancer screening.

cs.CV↗

arXiv2025

The impact of organic hazes and graphite on the observation of CO2-rich sub-Neptune atmospheres

Many sub-Neptune and super-Earth exoplanets are expected to develop metal-enriched atmospheres due to atmospheric loss processes such as photoevaporation or core-powered mass loss. Thermochemical equilibrium calculations predict that at high metallicity and a temperature range of 300-700 K, CO2 becomes the dominant carbon species, and graphite may be the thermodynamically favored condensate under low-pressure conditions. Building on prior laboratory findings that such environments yield organic haze rather than graphite, we measured the transmittance spectra of organic haze analogues and graphite samples, and computed their optical constants across the measured wavelength range from 0.4 to 25 μm. The organic haze exhibits strong vibrational absorption bands, notably at 3.0, 4.5, and 6.0 μm, while graphite shows featureless broadband absorption. The derived optical constants of haze and graphite provide the first dataset for organic haze analogues formed in CO2-rich atmospheres and offer improved applicability over prior graphite data derived from bulk reflectance or ellipsometry. We implemented these optical constants into the Virga and PICASO cloud and radiative transfer models to simulate transit spectra for GJ 1214b. The synthetic spectra with organic hazes reproduce the muted spectral features in the NIR observed by Hubble and general trends observed by JWST for GJ 1214b, while graphite models yield flat spectra across the observed wavelengths. This suggests haze features may serve as observational markers of carbon-rich atmospheres, whereas graphite's opacity could lead to radius overestimation, offering a possible explanation for super-puff exoplanets. Our work supplies essential optical to infrared data for interpreting observations of CO2-rich exoplanet atmospheres.

astro-ph.EP↗

arXiv2025

Formation of organic hazes in CO$_2$-rich sub-Neptune atmospheres within the graphite-stability regime

Super-Earths and sub-Neptunes are the most common exoplanets, with a "radius valley" suggesting that super-Earths may form by shedding sub-Neptunes' gaseous envelopes. Exoplanets that lie closer to the super-Earth side of the valley are more likely to have lost a significant fraction of their original H/He envelopes and become enriched in heavier elements with CO$_2$ gaining in abundance. It remains unclear which types of haze would form in such atmospheres, potentially significantly affecting spectroscopic observations. To investigate this, we performed laboratory simulations of two CO$_2$-rich gas mixtures (with 2000 times solar metallicity at 300 K and 500 K). We found that under plasma irradiation, organic hazes were produced at both temperatures with higher haze production rate at 300 K probably because condensation occurs more readily at lower temperature. Gas-phase analysis demonstrates the formation of various hydrocarbons, oxygen- and nitrogen-containing species, including reactive gas precursors like C$_2$H$_4$, CH$_2$O, and HCN, for haze formation. The compositional analysis of the haze particles reveals various functional groups and molecular formulas in both samples. The 500 K haze sample has larger average molecular sizes, higher degree of unsaturation with more double or triple bonds presence, and higher nitrogen content incorporated as N-H, C=N bonds, indicating different haze formation pathways. These findings not only improve the haze formation theories in CO$_2$-rich exoplanet atmospheres but also offer important implications for the interpretation of future observational data.

astro-ph.EP↗

arXiv2025

Inkjet Printed Liquid Crystal Droplet for Complex Beam Manipulation

The inkjet-fabricated liquid crystal (LC) droplet device not only capitalizes on the intrinsic birefringence properties of liquid crystals but also leverages the hemispherical shape of droplet devices on substrates. This configuration facilitates self-alignment of the LC director under the influence of surface tension. The LC droplet devices we fabricated are capable of intricate beam manipulation, encompassing both generation and analysis of light beams. Such devices possess substantial prospective applications in the fields of optical communications and light beam characterization, highlighting their significant potential for advancement in optical technologies.

physics.optics↗