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Researcher profile

Zhirui Li

Zhirui Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligenceastro-ph.IMastro-ph.SRMachine Learning

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Published work

2 published item(s)

preprint2026arXiv

A Large and Precise All-Sky Photometric Standard Star Dataset Across More Than 200 Passbands

High-precision photometric standard stars play a key role in enabling accurate photometric calibration and advancing various fields of astronomy. However, due to limitations in calibration methods and the limited availability and underuse of high-precision reference data, existing photometric standard stars may suffer from insufficient numbers, systematic errors exceeding 10 milli-magnitude (mmag), limited photometric band coverage, or incomplete sky coverage, among other issues. To overcome these limitations, we have constructed the largest (over 200 million stars, 1000 times the widely recognized Landolt standards in the same magnitude range), most precise (better than 10 mmag), and most comprehensive (over 200 bands, nearly 40 times the coverage of traditional standards) all-sky standard stars. Based on standards, we have calibrated multiple survey datasets to mmag precision, and subsequently developed a complete sky distribution of stars for the Pan-STARRS system. This database, the BEst STars Database (BEST), is expected to pave the way for achieving mmag-level - or even higher - photometric precision in large-scale surveys, and to play a central role in shaping a high-precision astronomical measurement framework.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Disentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data

Real-world clinical data is inherently multimodal, providing complementary evidence that mirrors the practical necessity of jointly assessing multiple related outcomes. Although multi-task learning can improve efficiency by sharing information across outcomes, existing approaches often fail to balance shared representation learning with outcome-specific modeling. Hard parameter sharing can trigger negative transfer when task gradients conflict, while flexible sharing may still entangle shared and task-specific signals. To address this, we propose a multi-task framework built on a unified Transformer for multimodal fusion, augmented with Orthogonal Task Decomposition (OrthTD) to split patient representations into shared and task-specific subspaces and impose a geometric orthogonality constraint to reduce redundancy and isolate task-specific signals. We evaluated OrthTD on a real-world cohort of 12,430 surgical patients for predicting four outcomes. OrthTD achieved average AUC (area under the receiver operating characteristic curve) of 87.5% and average AUPRC (area under the precision-recall curve) of 37.2%, consistently outperformed advanced tabular and multi-task methods. Notably, OrthTD achieves substantial gains in AUPRC, indicating superior performance in identifying rare events within imbalanced clinical data. These results suggest that enforcing non-redundant shared and task-specific representations can improve multi-outcome prediction from multimodal clinical data.

0 citations0 reviewsNo code linkedOpen access