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Ran Zhao

Publications and source records attributed to Ran Zhao.

At least 19 recordsLinked to original sources
arXiv2026

Element priors and target support shape chemical transfer in materials graph networks

Materials graph neural networks must often transfer to chemical regions weakly represented in training data. Such transfer can rely on predefined relations among elements or supervised evidence from target-containing structures, but these pathways are usually entangled. Here, held-out-element splits and incremental target support separate their roles. Without target-containing training structures, formation-energy errors depend strongly on the element representation, particularly for H, O and F. Matched perturbations show that representation-induced sharing matters beyond input dimension or numerical form, while a label-free similarity-graph prior reduces selected zero-shot errors. Adding a few target-containing structures sharply lowers errors and contracts differences among one-hot, k-hot and continuous inputs across ALIGNN and CGCNN. Calibration explains only part of this recovery, and freezing the initial element projection preserves most gains in five of six ALIGNN splits. Target support therefore shifts chemical transfer from reliance on static element relations toward learning from target-containing environments.

cond-mat.mtrl-sci↗
arXiv2026

Characteristic Mode Analysis of Composite Nanostructures using a Coupled System of Volume Integral and Hydrodynamic Equations

Full-structure and sub-structure characteristic mode analysis (CMA) formulations are developed for composite metallic--dielectric nanostructures based on a coupled system of volume integral equations (VIE) and the hydrodynamic equation (HDE). In the full-structure CMA, the generalized eigenvalue equation (GEE) is constructed from the matrix of the complete coupled system, and the resulting characteristic currents describe the response of the entire composite nanostructure. In the sub-structure CMA, the GEE is constructed from a reduced system, derived from the coupled system by eliminating the dielectric-region unknowns, so that it is expressed only in terms of the metallic-region currents. This isolates the resonances of the metallic region while still incorporating the effect of the dielectric region through the reduced system. Because the reduced system has a smaller dimension, the sub-structure CMA is computationally more efficient than the full-structure CMA and, when the dielectric does not resonate in the frequency range of interest, identifies the same resonances. Both formulations are validated against extinction cross-section (ECS) results and are used to characterize how a dielectric environment reshapes the metallic resonances, including substrate-induced red-shifts and, for high-contrast substrates in direct contact, hybridized modal responses.

physics.comp-ph↗
arXiv2026

Characteristic Mode Analysis of Plasmonic Nanostructures Using Hydrodynamic Volume Integral Equation

Metallic nanostructures confine electromagnetic fields at subwavelength scales, making them attractive as plasmonic nanoantennas. At these scales, the response of metals becomes nonlocal, and the hydrodynamic model is widely used to capture this response. However, existing solvers provide only the response to a prescribed excitation and do not directly reveal the intrinsic resonances of the structure. This work extends the characteristic mode analysis to plasmonic nanostructures to enable excitation-independent modal analysis of their resonant behavior. For simple metals, the coupled hydrodynamic and volume integral equations are reduced to a single hydrodynamic volume integral equation in terms of the induced current. The equation is discretized and cast as a generalized eigenvalue problem within the characteristic mode analysis framework, whose solution yields the characteristic mode currents and modal significance curves of the structure. The proposed framework is validated through three metallic nanostructures: a nanosphere, a nanorod, and a nanodimer. The results show that the method identifies the intrinsic resonances of each structure, including resonances not excited by a given source and additional resonances arising from the nonlocal response, which are absent in local models. The proposed framework provides physical insight into the modal mechanisms of plasmonic nanostructures and serves as a practical tool for their analysis and design.

physics.comp-ph↗
arXiv2026

A Single-Trace Surface Integral Equation Solver for Simulation of Open Bianisotropic Metasurfaces Described by Generalized Sheet Transition Conditions

A single-trace surface integral equation (SIE) solver incorporating generalized sheet transition conditions (GSTCs) is presented for the simulation of three-dimensional (3D) open bianisotropic metasurfaces. The metasurface is modeled as an infinitesimally thin, non-enclosing sheet across which the GSTCs enforce the electromagnetic field discontinuities through four surface susceptibility tensors. The proposed solver uses a single set of equivalent surface currents on the sheet, in place of the two sets used by prior multi-trace formulations. The scattered fields on both faces of the sheet, expressed through SIE operators acting on these currents, are substituted into the GSTCs. The resulting system of equations is then discretized using Rao--Wilton--Glisson basis functions. This solver models an open metasurface directly, without an artificial closure, and applies to both planar and curved geometries. It is validated against analytical solutions for polarization rotation and perfect reflection, and is used to model a realistic broadband absorber whose susceptibility tensors are retrieved from full-wave simulation data. A direct comparison shows that the single-trace formulation attains lower error than a multi-trace formulation while using significantly fewer unknowns.

physics.comp-ph↗
arXiv2026

Knowledge Visualization: A Benchmark and Method for Knowledge-Intensive Text-to-Image Generation

Recent text-to-image (T2I) models have demonstrated impressive capabilities in photorealistic synthesis and instruction following. However, their reliability in knowledge-intensive settings remains largely unexplored. Unlike natural image generation, knowledge visualization requires not only semantic alignment but also strict adherence to domain knowledge, structural constraints, and symbolic conventions, exposing a critical gap between visual plausibility and scientific correctness. To systematically study this problem, we introduce KVBench, a curriculum-grounded benchmark for evaluating knowledge-intensive T2I generation. KVBench covers six senior high-school subjects: Biology, Chemistry, Geography, History, Mathematics, and Physics. The benchmark consists of 1,800 expert-curated prompts derived from over 30 authoritative textbooks. Using this benchmark, we evaluate 14 state-of-the-art open- and closed-source models, revealing substantial deficiencies in logical reasoning, symbolic precision, and multilingual robustness, with open-source models consistently underperforming proprietary systems. To address these limitations, we further propose KE-Check, a two-stage framework that improves scientific fidelity via (1) Knowledge Elaboration for structured prompt enrichment, and (2) Checklist-Guided Refinement for explicit constraint enforcement through violation identification and constraint-guided editing. KE-Check effectively mitigates scientific hallucinations, narrowing the performance gap between open-source and leading closed-source models. Data and codes are publicly available at https://github.com/zhaoran66/KVBench.

cs.CV↗
arXiv2026

A Thin Sheet Volume Integral Equation Solver for Simulation of Bianisotropic Metasurfaces

A thin-sheet (TS) volume integral equation (VIE) formulation incorporating generalized sheet transition conditions (GSTCs) is presented for the simulation of three-dimensional (3D) bianisotropic metasurfaces. The metasurface is represented as an equivalent TS, with its constitutive tensors derived from the GSTC susceptibility tensors. Invoking the TS approximation, the governing VIEs are reduced to surface integral equations (SIEs), in which tangential and normal flux density components are treated as distinct sets of unknowns and discretized using Rao-Wilton-Glisson and pulse basis functions, respectively. In contrast to conventional GSTC approaches based on conventional SIEs, which represent only tangential fields, the proposed framework rigorously enforces the bianisotropic GSTCs, including normal field interactions, while retaining the flux-based VIE character of the formulation. Numerical examples demonstrate the accuracy and robustness of the proposed TS-VIE-GSTC solver for polarization rotation, perfect reflection, multi-directional attenuation, and oblique phase-shift transformation.

physics.comp-ph↗
arXiv2025

Learning Fair Decisions with Factor Models: Applications to Annuity Pricing

Fairness-aware statistical learning is essential for mitigating discrimination against protected attributes such as gender, race, and ethnicity in data-driven decision-making. This is particularly critical in high-stakes applications like insurance underwriting and annuity pricing, where biased business decisions can have significant financial and social consequences. Factor models are commonly used in these domains for risk assessment and pricing; however, their predictive outputs may inadvertently introduce or amplify bias. To address this, we propose a Fair Decision Model that incorporates fairness regularization to mitigate outcome disparities. Specifically, the model is designed to ensure that expected decision errors are balanced across demographic groups - a criterion we refer to as Decision Error Parity. We apply this framework to annuity pricing based on mortality modelling. An empirical analysis using Australian mortality data demonstrates that the Fair Decision Model can significantly reduce decision error disparity while also improving predictive accuracy compared to benchmark models, including both traditional and fair factor models.

stat.ME↗
arXiv2024

PDCFNet: Enhancing Underwater Images through Pixel Difference Convolution

Majority of deep learning methods utilize vanilla convolution for enhancing underwater images. While vanilla convolution excels in capturing local features and learning the spatial hierarchical structure of images, it tends to smooth input images, which can somewhat limit feature expression and modeling. A prominent characteristic of underwater degraded images is blur, and the goal of enhancement is to make the textures and details (high-frequency features) in the images more visible. Therefore, we believe that leveraging high-frequency features can improve enhancement performance. To address this, we introduce Pixel Difference Convolution (PDC), which focuses on gradient information with significant changes in the image, thereby improving the modeling of enhanced images. We propose an underwater image enhancement network, PDCFNet, based on PDC and cross-level feature fusion. Specifically, we design a detail enhancement module based on PDC that employs parallel PDCs to capture high-frequency features, leading to better detail and texture enhancement. The designed cross-level feature fusion module performs operations such as concatenation and multiplication on features from different levels, ensuring sufficient interaction and enhancement between diverse features. Our proposed PDCFNet achieves a PSNR of 27.37 and an SSIM of 92.02 on the UIEB dataset, attaining the best performance to date. Our code is available at https://github.com/zhangsong1213/PDCFNet.

cs.CV↗
arXiv2024

Mamba-UIE: Enhancing Underwater Images with Physical Model Constraint

In underwater image enhancement (UIE), convolutional neural networks (CNN) have inherent limitations in modeling long-range dependencies and are less effective in recovering global features. While Transformers excel at modeling long-range dependencies, their quadratic computational complexity with increasing image resolution presents significant efficiency challenges. Additionally, most supervised learning methods lack effective physical model constraint, which can lead to insufficient realism and overfitting in generated images. To address these issues, we propose a physical model constraint-based underwater image enhancement framework, Mamba-UIE. Specifically, we decompose the input image into four components: underwater scene radiance, direct transmission map, backscatter transmission map, and global background light. These components are reassembled according to the revised underwater image formation model, and the reconstruction consistency constraint is applied between the reconstructed image and the original image, thereby achieving effective physical constraint on the underwater image enhancement process. To tackle the quadratic computational complexity of Transformers when handling long sequences, we introduce the Mamba-UIE network based on linear complexity state space models. By incorporating the Mamba in Convolution block, long-range dependencies are modeled at both the channel and spatial levels, while the CNN backbone is retained to recover local features and details. Extensive experiments on three public datasets demonstrate that our proposed Mamba-UIE outperforms existing state-of-the-art methods, achieving a PSNR of 27.13 and an SSIM of 0.93 on the UIEB dataset. Our method is available at https://github.com/zhangsong1213/Mamba-UIE.

cs.AI↗
arXiv2024

UR4NNV: Neural Network Verification, Under-approximation Reachability Works!

Recently, formal verification of deep neural networks (DNNs) has garnered considerable attention, and over-approximation based methods have become popular due to their effectiveness and efficiency. However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question. To address this, this paper introduces the UR4NNV verification framework, which utilizes under-approximation reachability analysis for DNN verification for the first time. UR4NNV focuses on DNNs with Rectified Linear Unit (ReLU) activations and employs a binary tree branch-based under-approximation algorithm. In each epoch, UR4NNV under-approximates a sub-polytope of the reachable set and verifies this polytope against the given property. Through a trial-and-error approach, UR4NNV effectively falsifies DNN properties while providing confidence levels when reaching verification epoch bounds and failing falsifying properties. Experimental comparisons with existing verification methods demonstrate the effectiveness and efficiency of UR4NNV, significantly reducing the impact of the "unknown dilemma".

cs.AI↗
arXiv2023

An Efficient Iterative Least Squares Algorithm for Large-dimensional Matrix Factor Model via Random Projection

The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In this paper, we propose a simple iterative least squares algorithm for matrix factor models, in contrast to the Principal Component Analysis (PCA)-based methods in the literature. In detail, we first propose to estimate the latent factor matrices by projecting the observations with two deterministic weight matrices, which are chosen to diversify away the idiosyncratic components. We show that the inferences on factors are still asymptotically valid even if we overestimate both the row/column factor numbers. We then estimate the row/column loading matrices by minimizing the squared loss function under certain identifiability conditions. The resultant estimators of the loading matrices are treated as the new weight/projection matrices and thus the above update procedure can be iteratively performed until convergence. Theoretically, given the true dimensions of the factor matrices, we derive the convergence rates of the estimators for loading matrices and common components at any $s$-th step iteration. Additionally, we propose an eigenvalue-ratio method to estimate the pair of factor numbers consistently. Thorough numerical simulations are conducted to investigate the finite-sample performance of the proposed methods and two real datasets associated with financial portfolios and multinational macroeconomic indices are used to illustrate our algorithm's practical usefulness.

stat.ME↗
arXiv2023

Robust Statistical Inference for Large-dimensional Matrix-valued Time Series via Iterative Huber Regression

Matrix factor model is drawing growing attention for simultaneous two-way dimension reduction of well-structured matrix-valued observations. This paper focuses on robust statistical inference for matrix factor model in the ``diverging dimension" regime. We derive the convergence rates of the robust estimators for loadings, factors and common components under finite second moment assumption of the idiosyncratic errors. In addition, the asymptotic distributions of the estimators are also derived under mild conditions. We propose a rank minimization and an eigenvalue-ratio method to estimate the pair of factor numbers consistently. Numerical studies confirm the iterative Huber regression algorithm is a practical and reliable approach for the estimation of matrix factor model, especially under the cases with heavy-tailed idiosyncratic errors . We illustrate the practical usefulness of the proposed methods by two real datasets, one on financial portfolios and one on the macroeconomic indices of China.

stat.ME↗
arXiv2022

Sub-structure characteristic mode analysis of microstrip antennas using a global multi-trace formulation

A characteristic mode (CM) method that relies on a global multi-trace formulation (MTF) of surface integral equations is proposed to compute the modes and the resonance frequencies of microstrip patch antennas with finite dielectric substrates and ground planes. Compared to the coupled formulation of electric field and Poggio-Miller-Chang-Harrington-Wu-Tsai integral equations, global MTF allows for more direct implementation of a sub-structure CM method. This is achieved by representing the coupling of the electromagnetic fields on the substrate and ground plane in the form of a numerical Green function matrix, which yields a more compact generalized eigenvalue equation. The resulting sub-structure CM method avoids the cumbersome computation of the multilayered medium Green function (unlike the CM methods that rely on mixed-potential integral equations) and the volumetric discretization of the substrate (unlike the CM methods that rely on volume-surface integral equations), and numerical results show that it is a reliable and accurate approach to predicting the modal behavior of electromagnetic fields on practical microstrip antennas.

cs.CE↗
arXiv2022

A Coupled Hybridizable Discontinuous Galerkin and Boundary Integral Method for Analyzing Electromagnetic Scattering

A coupled hybridizable discontinuous Galerkin (HDG) and boundary integral (BI) method is proposed to efficiently analyze electromagnetic scattering from inhomogeneous/composite objects. The coupling between the HDG and the BI equations is realized using the numerical flux operating on the equivalent current and the global unknown of the HDG. This approach yields sparse coupling matrices upon discretization. Inclusion of the BI equation ensures that the only error in enforcing the radiation conditions is the discretization. However, the discretization of this equation yields a dense matrix, which prohibits the use of a direct matrix solver on the overall coupled system as often done with traditional HDG schemes. To overcome this bottleneck, a "hybrid" method is developed. This method uses an iterative scheme to solve the overall coupled system but within the matrix-vector multiplication subroutine of the iterations, the inverse of the HDG matrix is efficiently accounted for using a sparse direct matrix solver. The same subroutine also uses the multilevel fast multipole algorithm to accelerate the multiplication of the guess vector with the dense BI matrix. The numerical results demonstrate the accuracy, the efficiency, and the applicability of the proposed HDG-BI solver.

cs.CE↗
arXiv2022

Improved Multi-Dimensional Bee Colony Algorithm for Airport Freight Station Scheduling

Due to the rapid increase of air cargo and postal transport volume, an efficient automated multi-dimensional warehouse with elevating transfer vehicles (ETVs) should be established and an effective scheduling strategy should be designed for improving the cargo handling efficiency. In this paper, artificial bee colony algorithm, which possesses strong global optimization ability and fewer parameters, is firstly introduced to simultaneously optimize the route of ETV and the assignment of entrances and exits. Moreover, for further improve the optimization performance of ABC, novel full-dimensional search strategy with parallelization, and random multi-dimensional search strategy are incorporated in the framework of ABC to improve the diversity of the population and the convergence speed respectively. Our proposed algorithms are evaluated on several benchmark functions, and then applied to solve the combinatorial optimization problem with multitask, multiple entrances and exits in air cargo terminal. The simulations show that the proposed algorithms can achieve much more desired performance than the traditional artificial bee colony algorithm at balancing the exploitation and exploration abilities.

math.OC↗
arXiv2022

Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit

Automatic detection of polyps is challenging because different polyps vary greatly, while the changes between polyps and their analogues are small. The state-of-the-art methods are based on convolutional neural networks (CNNs). However, they may fail due to lack of training data, resulting in high rates of missed detection and false positives (FPs). In order to solve these problems, our method combines the two-dimensional (2-D) CNN-based real-time object detector network with spatiotemporal information. Firstly, we use a 2-D detector network to detect static images and frames, and based on the detector network, we propose two feature enhancement modules-the FP Relearning Module (FPRM) to make the detector network learning more about the features of FPs for higher precision, and the Image Style Transfer Module (ISTM) to enhance the features of polyps for sensitivity improvement. In video detection, we integrate spatiotemporal information, which uses Structural Similarity (SSIM) to measure the similarity between video frames. Finally, we propose the Inter-frame Similarity Correlation Unit (ISCU) to combine the results obtained by the detector network and frame similarity to make the final decision. We verify our method on both private databases and publicly available databases. Experimental results show that these modules and units provide a performance improvement compared with the baseline method. Comparison with the state-of-the-art methods shows that the proposed method outperforms the existing ones which can meet real-time constraints. It's demonstrated that our method provides a performance improvement in sensitivity, precision and specificity, and has great potential to be applied in clinical colonoscopy.

cs.CV↗
arXiv2022

Comparative study of Hermitian and non-Hermitian topological dielectric photonic crystals

The effects of gain and loss on the band structures of a bulk topological dielectric photonic crystal (PC) with $C_{6v}$ symmetry and the PC-air-PC interface are studied based on first-principle calculation. To illustrate the importance of parity-time (PT) symmetry, three systems are considered, namely the PT-symmetric, PT-asymmetric, and lossy systems. We find that the system with gain and loss distributed in a PT symmetric manner exhibits a phase transition from a PT exact phase to a PT broken phase as the strength of the gain and loss increases, while for the PT-asymmetric and lossy systems, no such phase transition occurs. Furthermore, based on the Wilson loop calculation, the topology of the PT-symmetric system in the PT exact phase is demonstrated to keep unchanged as the Hermitian system. At last, different kinds of edge states in Hermitian systems under the influences of gain and loss are studied and we find that while the eigenfrequencies of nontrivial edge states become complex conjugate pairs, they keep real for the trivial defect states.

physics.optics↗