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Xiaoxiao Sun

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Computer Vision cond-mat.mtrl-sci Methodology physics.app-ph

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preprint2026arXiv

WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild

Using multimodal foundation models to analyze table images is a high-value yet challenging application in consumer and enterprise scenarios. Despite its importance, current evaluations rely largely on structured-text tables or clean rendered images, leaving the visual complexity of in-the-wild table images underexplored. Such images feature varied layouts and diverse domains that demand sophisticated structural perception and numerical reasoning. To bridge this gap, we introduce WildTableBench, the first question-answering benchmark for naturally occurring table images from real-world settings. WildTableBench comprises 402 high-information-density table images collected from online forums and websites across diverse domains, together with 928 manually annotated and verified questions spanning 17 subtypes across five categories. We evaluate 21 frontier proprietary and open-source multimodal foundation models on this benchmark. Only one model exceeds 50% accuracy, while all remaining models range from 4.1% to 49.9%. We further conduct diagnostic analyses to characterize model failures and reveal persistent weaknesses in structural perception and reasoning. These results and analyses provide useful insights into current model capabilities and establish WildTableBench as a valuable diagnostic benchmark for table image understanding.

preprint2021arXiv

Triple-cation perovskite solar cells fabricated by hybrid PVD/blade coating process using green solvents

The scalability of highly efficient organic-inorganic perovskite solar cells (PSCs) is one of the remaining challenges of solar module manufacturing. Various scalable methods have been explored to strive for uniform perovskite films of high crystal quality on large-area substrates. However, each of these methods have individual drawbacks, limiting the successful commercialization of perovskite photovoltaics. Here, we report a fully scalable hybrid process, which combines vapor- and solution-based techniques to deposit high quality uniform perovskite films on large-area substrates. This two-step process does not use toxic solvents, and it further allows facile implementation of passivation strategies and additives. We fabricated PSCs based on this process and used blade coating to deposit both charge transporting layers (SnO2 and Spiro-OMeTAD) without hazardous solvents in ambient air. The fabricated PSCs have yielded open-circuit voltage up to 1.16 V and power conversion efficiency of 18.7 % with good uniformity on 5 cm x 5 cm substrates.

preprint2020arXiv

An Asympirical Smoothing Parameters Selection Approach for Smoothing Spline ANOVA Models in Large Samples

Large samples have been generated routinely from various sources. Classic statistical models, such as smoothing spline ANOVA models, are not well equipped to analyze such large samples due to expensive computational costs. In particular, the daunting computational costs of selecting smoothing parameters render smoothing spline ANOVA models impractical. In this article, we develop an asympirical, i.e., asymptotic and empirical, smoothing parameters selection approach for smoothing spline ANOVA models in large samples. The idea of this approach is to use asymptotic analysis to show that the optimal smoothing parameter is a polynomial function of the sample size and an unknown constant. The unknown constant is then estimated through empirical subsample extrapolation. The proposed method significantly reduces the computational costs of selecting smoothing parameters in high-dimensional and large samples. We show smoothing parameters chosen by the proposed method tend to the optimal smoothing parameters that minimise a specific risk function. In addition, the estimator based on the proposed smoothing parameters achieves the optimal convergence rate. Extensive simulation studies demonstrate the numerical advantage of the proposed method over competing methods in terms of relative efficacies and running time. On an application to molecular dynamics data with nearly one million observations, the proposed method has the best prediction performance.