arXiv subjects

Chao He

Publications and source records attributed to Chao He.

At least 19 recordsLinked to original sources

arXiv2026

A Cross-Laboratory Comparison Study of Titan Haze Analogs: Surface Energy II

The organic haze particles on Titan play important roles in atmospheric cloud formation and aerosol-lake interactions. These processes are strongly influenced by the surface energy of the haze particles, which controls cohesion and wetting behavior. This study presents a comparative analysis of 32 laboratory-produced haze analog samples ("tholins") synthesized across three laboratories. Using contact angle measurements, we determine the total surface energy and its dispersive and polar components for all samples, systematically evaluating the effects of substrate choice, air exposure, initial N2/CH4 gas mixture, and experimental setup. We find that tholin samples exhibit minimal substrate dependence, whereas exposure to ambient air substantially modifies the surface chemistry, altering the balance between dispersive and polar components. Thus, future Titan-relevant surface property measurements may use any substrate but must keep samples pristine. Surface energies vary weakly across methane concentrations, from 1-10% CH4 in N2, indicating that Titan's hazes formed at different altitudes should exhibit broadly similar cohesiveness. In contrast, experimental conditions such as gas exposure time and energy source produce the dominant differences in surface energy, driven largely by variations in polar components. Despite these differences, tholin samples exhibit high dispersive components, implying that Titan's hazes should act as efficient cloud condensation nuclei for hydrocarbon clouds and should generally sink into Titan's lakes. Given observed ethane ice clouds, we conclude that cold plasma tholins may be better physical analogs for Titan's hazes than samples produced with far-ultraviolet irradiation, though intrinsic surface energies of UV tholins remain uncertain due to film thickness limitations.

astro-ph.EP↗

arXiv2026

Defects encode high-dimensional topological information

In polarization fields, Stokes skyrmions are continuous vectorial textures that encode integer-valued topological invariants across real space, enabling robust optical information encoding under complex perturbations. This topological resilience, however, fails when singular points occur where the Stokes vector has no unique limiting value, placing a fundamental constraint on skyrmion-based information manipulation. Here, we show, paradoxically, that the very defects that destroy conventional resilience can become the carriers of topological information. We introduce the resulting structures as Stokes defect skyrmions, in which singular Stokes responses constitute measurable topological degrees of freedom with theoretically minimal size. We design and realize one class of them using all-dielectric metasurfaces that combine arbitrarily controlled distinguished fast-axis singularities with customized retardance profiles. The resulting fields are then described by high-dimensional integer-valued topological tuples, providing theoretically unbounded information capacity at the nanoscale. As a proof-of-concept demonstration, selected tuple components are mapped to represent predefined alphabetic symbols, realizing controlled high-dimensional information representation within a single optical field. Our results establish Stokes defects as functional units for higher-dimensional topological encoding, expanding the role of defects from failure points to engineerable carriers of optical information.

physics.optics↗

arXiv2026

Do Maps Still Matter for Machines: Revisiting the Role of Choropleth Maps in Foundation Model Spatial Understanding

Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information. This study examines whether choropleth maps remain useful for machine spatial understanding when models can directly process structured geodata. We introduce ChoroplethMap-Bench, a controlled benchmark containing 2,400 synthetic choropleth maps, corresponding GeoJSON data, and 12,000 questions across five cognitive dimensions: Identify, Spatial Recognition, Compare, Rank, and Delineate. We evaluate 22 open-source and proprietary models under three input conditions: Data Only, Map Only, and Data + Map. The results show that maps substantially improve spatial reasoning, especially when combined with symbolic data and for tasks requiring higher-level understanding of spatial patterns. We further analyze the effects of map type, color hue, and spatial structure, as well as prompting strategies, language, geographic context, decoding settings, classification methods, and response stability. Overall, the Data + Map condition achieves the strongest performance, demonstrating that maps remain valuable external representations for foundation model spatial reasoning.

cs.AI↗

arXiv2026

ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts

Foundation models are reshaping computational pathology, yet their capabilities remain shaped by pretraining objectives, data sources, and spatial scales, fragmenting complementary expertise across separate backbones. Here we present ALICE, a unified foundation model trained through multi-stage agglomerative distillation that sequentially distills eight vision-only, vision-language, and slide-level teacher models into dedicated modules of a single backbone. ALICE is pretrained on 24,985,184 tile-level pathology images and 155,604 high-resolution images, and evaluated across 21 task scenarios, 96 downstream tasks, and 48 data sources, spanning region-of-interest tissue analysis, vision-language multimodal evaluation, and whole-slide clinical assessment. In all three evaluation settings, ALICE achieved the best average rank among task-matched pathology foundation models. These results demonstrate that agglomerative distillation can consolidate complementary capabilities from specialized models into a unified backbone for broad computational pathology applications. The model is available at https://github.com/WonderLandxD/ALICE.

cs.CV↗

arXiv2026

Charge-partition pathways in strong-field photoionization of carbonyl sulfide monomers and dimers

Strong-field photoionization of molecules and molecular clusters gives rise to a rich variety of fragmentation pathways governed by charge localization and redistribution on ultrafast timescales. Here, we report a velocity-map imaging study of the strong-field photoionization and fragmentation of carbonyl sulfide (OCS) monomers and dimers driven by 150 femtosecond (fs) laser pulses at 775~nm. The images of the total kinetic-energy and angular distributions of the OCS$^{2+}$, S$^+$, and CO$^+$ fragments were interpreted with the help of electronic-structure calculations of the potential energy surfaces for OCS$^+$ and OCS$^{2+}$. We identify distinct dissociation pathways of singly and doubly ionized OCS, including two-body breakup channels of OCS$^+$ into $\mathrm{S}^+ + \mathrm{CO}$ and $\mathrm{CO}^+ + \mathrm{S}$, dissociation of OCS$^{2+}$ into $\mathrm{S}^+ + \mathrm{CO}$$^+$ as well as higher-order three-body fragmentation. In addition, the images of the OCS$^{2+}$ channel exhibit near-zero-momentum components, low-energy isotropic features, and highly anisotropic contributions at high kinetic energies that cannot be explained by monomer ionization alone. Analysis of the KER distributions and angular anisotropies indicates that these features originate from the breakup of multiply charged OCS dimers ((OCS)$_2^{2+}$, (OCS)$_2^{3+}$, and (OCS)$_2^{4+}$) through charge-separation channels. Our results illustrate how dynamic signatures of strong-field fragmentation evolve from intramolecular dissociation in isolated molecules to intermolecular charge separation in weakly bound clusters providing a unified picture of charge-driven dissociation dynamics beyond the single-molecule limit.

physics.chem-ph↗

arXiv2026

TeRoR: Decoupled Temporal Rotation with Relational Circular Region for Temporal Knowledge Graph Embedding

In recent years, with the emergence of Temporal Knowledge Graphs (TKGs), research on learning entity and relation representations in TKGs has attracted increasing attention, giving rise to a large number of TKG embedding methods. TeRo is a simple and efficient temporal knowledge graph embedding approach. However, TeRo does not do well in modeling the mapping properties of various relations, such as one-to-many, many-to-one, and many-to-many. Meanwhile, it also has limitations in the expression of temporal information. To address these issues, we propose a novel TKG embedding method named TeRoR. This method divides the temporal evolution of entity embeddings, and conducts independent rotation transformations on head and tail entities in the complex vector space to strengthen temporal information modeling capacity. In terms of relational characteristics, we train a radius to constrain the rotated and translated head entities within a circular region centered on the tail entity, which effectively captures the diverse mapping properties of relations. Experimental results demonstrate that TeRoR achieves competitive performance against state-of-the-art models on four distinct TKG datasets.

cs.LG↗

arXiv2026

RelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion

Real-world knowledge graphs are often incomplete, lacking many valid facts. Knowledge Graph Completion (KGC) aims to predict missing links using known triples, thereby enhancing graph coverage. A key challenge is modeling diverse relational patterns such as symmetry, antisymmetry, inversion, composition and semantic hierarchy. Existing models such as RotatE can capture symmetric, antisymmetric, inverse, and commutative composition patterns, yet struggle with non-commutative composition. Rotate3D addresses this by introducing non-commutativity via three-dimensional rotations, but still fails to capture the semantic hierarchies prevalent in knowledge graphs. Moreover, both models cannot effectively model one-to-many relations. To overcome these limitations, we propose RelBall, which extends Rotate3D with two innovations. First, our model introduces modulus transformation to model hierarchies, driving abstract concepts toward smaller moduli and concrete instances toward larger ones. Second, it introduces a tail-centric relation ball to model one-to-one, one-to-many, many-to-one, and many-to-many relations. RelBall offers the following advantages: (1) coverage of all relational patterns, including the ones mentioned above; (2) an interpretable hierarchical representation where the modulus directly reflect semantic levels; (3) support for one-to-one, one-to-many, many-to-one, and many-to-many relations. Experiments on multiple datasets demonstrate RelBall's competitive link prediction performance against various baselines.

cs.AI↗

arXiv2026

Workflow-GYM: Towards Long-Horizon Evaluation of Computer-use Agentic tasks in Real-World Professional Fields

Recent years have witnessed the rapid evolution of AI agents toward handling increasingly complex, real-world tasks. However, existing benchmarks rarely evaluate whether agents can operate graphical user interfaces to complete long-horizon, high-value professional workflows across diverse domains. Current GUI benchmarks still predominantly focus on general-purpose software, relatively simple applications, and short-horizon tasks, leaving it largely unknown whether modern agents can follow user instructions to autonomously operate domain-specific professional software and accomplish economically valuable work in an end-to-end manner. To bridge this gap, we introduce Workflow-GYM, a benchmark for long-horizon GUI tasks centered on professional domains and specialized software environments. Through extensive experiments on state-of-the-art models, we find that even the strongest models achieve only slightly above 30% success rates, highlighting that professional long-horizon GUI workflows remain highly challenging for current GUI agents. Further analysis reveals that current agents struggle to maintain long-horizon workflow consistency, frequently exhibiting workflow stage omission, error propagation, objective drift, and insufficient understanding of professional software environments. Our findings provide important insights into the limitations of current agent systems and suggest key directions for the next generation of GUI-agent research.

cs.AI↗

arXiv2026

Ultraviolet Radiation Effects on the Optical Properties of Water-Dominated Exoplanet Hazes

Temperate sub-Neptune and terrestrial exoplanets could contain large inventories of water in various phases, such as water-dominated atmospheres or even oceans. Observations have shown that many exoplanets, including water worlds, likely contain photochemically-generated hazes. Haze particles are a key source of organic matter and may impact the evolution or origin of life; their optical properties are imperative for interpreting observations through theoretical atmospheric modeling. Modelers have thus far assumed haze optical properties that may not represent hazes under sub-Neptune and terrestrial atmospheric conditions. Often orbiting close to M-dwarf stars, these planets receive large amounts of radiation, especially during flaring events, which may accelerate atmospheric escape and affect atmospheric compositions. Here, we present optical constants of experimentally-generated sub-Neptune haze analogs before and after UV irradiation across a broad wavelength range (0.5 to 8 {\mu}m). We find that UV-irradiation alters haze optical constants which become generally more absorbing in this wavelength range, which we hypothesize is due to our sample containing more oxygen-rich absorbing bands post irradiation. We use Virga and PICASO to simulate transmission spectra of potentially hazy water-dominated planets GJ 1214b and LHS 1140b, accounting for irradiated haze layers in their atmospheres. For our GJ 1214b CH4-rich haze modeled case, we see a difference in the N-H feature at 2.6 {\mu}m in the resulting transmission spectrum between irradiated and unaltered haze that should be observable within current JWST capabilities. Broadly, we demonstrate the importance of using more representative optical constants, as they have an impact on current and future atmospheric composition interpretations.

astro-ph.EP↗

arXiv2026

Integrated photonic computing: towards high-dimensional information processing

The rapid growth of artificial intelligence, coupled with the slowing of Moore's law, is straining computing infrastructure, as CMOS electronics face inherent limits in bandwidth, energy efficiency, and parallelism. Integrated photonic computing encodes and processes information using the phase, amplitude, spatial modes, wavelength channels, and polarisation of guided optical fields, offering a scalable and energy-efficient route beyond charge-based signalling. Here, we review on-chip photonic computing, emphasising the progression from low-dimensional to high-dimensional architectures. At the foundational level, low-dimensional approaches manipulate the phase and amplitude of guided light through Mach-Zehnder interferometers, diffractive structures, microring resonators, and absorptive elements, forming a programmable basis for optical matrix-vector multiplication. Crucially, high-dimensional architectures exploit spatial modes and wavelength channels to carry multiple independent data streams through a single waveguide, achieving higher throughput with moderate hardware overhead. Practical deployment, however, demands more than device innovation. We examine how system-level techniques, from time-wavelength interleaving to hardware-aware training, address energy efficiency, precision, and algorithm-hardware co-design. Five challenges nevertheless remain: electro-optic conversion efficiency, computing parallelism, spatial integration, reconfigurability, and robustness. We highlight emerging topological structures, such as optical skyrmions, as a promising route to fault-tolerant, topologically protected encoding that exploits the largely untapped polarisation degree of freedom. We argue that, by embracing the higher dimensionality of light, photonic computing can offer not merely an incremental improvement but a new paradigm for high-performance, energy-efficient information processing.

physics.optics↗

arXiv2026

Drop-on-demand printed negative dielectric anisotropy liquid crystal droplets for adaptive complex beam manipulation and assessment

Adaptive manipulation of vectorial optical fields are important for optical metrology, imaging, and structured light related applications, yet existing approaches often rely on bulky or sequentially operated systems. Here we demonstrate an inkjet-printed negative dielectric anisotropy nematic liquid crystal droplet platform that unifies adaptive complex beam generation and full vectorial optical field sensing within a single printed architecture. For complex beam generation, voltage-driven director reconfiguration in the droplets produces tunable birefringence and wavelength-dependent polarization textures, including skyrmionic like optical fields. For adaptive full vectorial optical field sensing, the same droplet array enables spectral and polarization retrieval through wavelength-dependent intensity patterns and division-of-wavefront polarimetry, while also functioning as a microlens array for Shack Hartmann wavefront sensing to reconstruct phase. These results establish negative dielectric anisotropy liquid crystal droplets as a scalable soft-matter photonic system for adaptive beam manipulation and multidimensional optical field characterization.

physics.optics↗

arXiv2026

Seasonal Variability of Pluto's Haze Formation Revealed by Laboratory Simulations

Pluto possesses a thin atmosphere primarily composed of N2, with minor constituents including CO and CH4. Photochemical processes generate distinct haze layers as observed by the New Horizons spacecraft. However, the mechanisms governing haze formation, as well as the composition and physical properties of the hazes, remain poorly constrained. Due to Pluto's highly eccentric orbit and obliquity, its surface temperature and atmospheric composition undergo substantial seasonal variations, but it is unclear how such seasonal variations impact the chemical pathways and efficiency of haze formation in Pluto's atmosphere. To address this, we conducted a laboratory simulation of Pluto's atmospheric photochemistry, in which N2/CH4/CO gas mixtures with CH4 concentrations varying from 0.1% to 5% were exposed to a glow discharge to initiate photochemical reactions. Gas-phase composition was monitored in situ using a residual gas analyzer (RGA), while the solid-phase products were characterized by atomic force microscopy (AFM), a gas pycnometer, infrared spectroscopy (IR), and very high-resolution mass spectrometry (VHRMS) to determine particle sizes, density, and composition, respectively. Our results show that increasing the CH4 mixing ratio significantly enhances the yield of gas and solid products. Under low CH4 conditions, nitrogen is primarily incorporated into solids as cyanide groups; whereas CH4-rich conditions favor the formation of amino groups, greatly promoting nitrogen incorporation into organic solids. These findings not only shed light on how seasonal variations into Pluto's atmosphere composition influence haze formation pathways, but also provide critical parameters to interpret observational data and to improve photochemical and microphysical models of planetary hazes.

astro-ph.EP↗

arXiv2026

Hydrolyzed Hazes on Water-rich Exoplanets: Optical Constants and Detectability

Observations of temperate sub-Neptunes suggest active chemical environments, finding evidence of both water vapor and photochemical hazes in their atmospheres. Hazes formed in water-rich atmospheres are chemically complex, containing molecules relevant to prebiotic chemistry, and their strong optical opacity obscures sought-after gaseous molecular absorption features. While many studies have investigated haze formation and properties across diverse atmospheric conditions, little is known about the evolution of these hazes in their environment once formed. In particular, interactions with water can drive hydrolysis reactions that alter haze composition and optical behavior, affecting our interpretations of habitability and observational spectroscopy. Here, we perform hydrolysis experiments on haze analogs of temperate water-rich exoplanets and measure their optical properties. Transmittance measurements from 0.4 to 28.5 $\mu$m reveal changes in key functional groups after hydrolysis, along with an overall increase in sample absorbance. We report the derived optical constants for use in observational and modeling studies. Through synthetic atmospheric spectra, we demonstrate the need for physically informed haze optical properties in models, consistent with expected planetary conditions. The increased absorptivity and high imaginary refractive index of hydrolyzed hazes almost completely flatten features in model spectra, presenting critical consequences for atmospheric characterization of water-rich sub-Neptunes.

astro-ph.EP↗

arXiv2026

Influence of CO versus CH$_4$ on organic haze formation in atmospheres of diverse terrestrial exoplanets

Context. Terrestrial exoplanets are expected to host secondary, high-metallicity atmospheres derived from outgassing of volatiles such as N2, CO2, H2O, CH4, and CO. Photochemical organic hazes are likely to form in such environments, significantly affecting atmospheric observations and planetary habitability. Aims. We investigate haze formation in representative terrestrial exoplanet atmospheres and assess how CH4 versus CO as the primary carbon source affects haze production rates, particle properties, and chemical complexity. Methods. We performed six laboratory simulations by exposing gas mixtures at a few mbar to glow discharge at 300 K. Each atmosphere contained 75% N2, CO2, or H2O, 10% of each of the other two gases, and 5% CH4 or CO. Gas-phase products were analyzed with a residual gas analyzer, and solid products were characterized by production rate, particle density, atomic force microscopy, Fourier-transform infrared spectroscopy, and very high-resolution mass spectrometry. Results. CH4 experiments produced more diverse gas-phase species and much higher haze yields than the corresponding CO experiments. CO-derived hazes showed a narrow particle size range of 10-80 nm, whereas CH4-derived hazes were denser and chemically more complex. The identified molecular formulas suggest growth pathways linked to gaseous precursors such as HCN, CH2O, and C2H4. Conclusions. The atmospheric redox state critically controls haze formation in simulated terrestrial exoplanet atmospheres. CH4 is significantly more effective than CO in initiating organic growth, leading to higher haze production rates and greater chemical complexity. These results provide useful constraints for exoplanet atmospheric modeling and spectral interpretation, and further support the possibility that reducing atmospheres may facilitate prebiotic organic chemistry relevant to the emergence of life.

astro-ph.EP↗

arXiv2026

Quantifying Building Blocks of Life in Planetary Analog Materials: Implications for Prebiotic Chemistry and Biosignature Identification

Building blocks of life such as amino acids, nucleobases, and fatty acids are central to prebiotic chemistry and represent key targets in the search for planetary biosignatures. In planetary materials, biomolecules typically occur at trace levels within complex matrices, posing substantial analytical challenges, particularly for quantitative characterization. Here we develop a gas chromatography tandem mass spectrometry method that enables robust qualitative and quantitative analysis of 56 prebiotically relevant molecules. The method is applied to a Titan aerosol analog and, for the first time, to a Martian gypsum analog from the Qaidam Basin, revealing diverse inventories of amino acids, nucleobases, and fatty acids in both samples. In the Titan aerosol analog, the first detection of phenylalanine and an extensive inventory of fatty acids, together with elevated nucleobase abundances, offers new insights into atmospheric photochemical synthesis of prebiotic molecules. In the Martian analog sample, amino acids are detectable and exhibit pronounced biotic abiotic contrasts in abundance patterns relative to those observed in the Titan aerosol analog, whereas fatty acids show more overlapping abiotic and biotic signatures, highlighting the potential of amino acids as robust biosignatures. These results provide quantitative constraints on prebiotic chemical evolution and underscore the utility of GC-MS-MS for biosignature identification in planetary exploration.

astro-ph.EP↗

arXiv2026

ProcureGym: A Multi-Agent Markov Game Framework for Modeling National Volume-based Drug Procurement

In this paper, we introduce ProcureGym, an data-driven multi-agent simulation platform that models China's National Volume-Based drug Procurement (NVBP) as a Markov Game. Based on real-world data from 7 rounds of NVBP (covering 325 drugs and 2,267 firms), the platform establishes a high-fidelity simulation environment. Within this framework, we evaluate diverse agent models, including Reinforcement Learning (RL), Large Language Model (LLM), and Rule-based algorithms. Experimental results demonstrate that RL agents achieve superior winner alignment and profits. Further analyses show that maximum valid bidding price and procurement volume dominate strategic outcomes. ProcureGym thus serves as a rigorous instrument for assessing policy impacts and formulating future procurement strategies.

cs.SI↗

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

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↗