arXiv subjects

Zhirui Li

Publications and source records attributed to Zhirui Li.

At least 19 recordsLinked to original sources

arXiv2026

Understanding From Human Perspective: A Multi-agent System for Interactive Egocentric Medical Image Segmentation

Interactive egocentric medical image segmentation (IEMIS) plays an important role in smart-glasses-assisted medical image review, segmenting the medical targets a clinician refers to from their egocentric view. Once it succeeds, the object-level visual evidence it provides strengthens the review and underpins fine-grained analysis and clinical decision-making. However, the instruction and the video both come from the user's egocentric perspective, which poses two challenges. (1) Semantic ambiguity leaves the model unable to confirm the user-intended target. (2) Visual variability makes the segmentation jump from frame to frame. In this paper, we propose EgoMed-Agent, a multi-agent system that understands the target from the human perspective through two workflows. (1) The \textit{Target Confirmation Workflow} grounds the instruction against candidate targets with a reliability score, confirming the target when the grounding is reliable and asking the user to clarify when it is not, thereby confirming the segmentation target. (2) The \textit{Localization-Guided Propagation Workflow} couples mask propagation with per-frame target localization, using the localized target to correct the propagated mask whenever the two diverge, so the segmentation stays on the target across the egocentric video. Extensive experiments show that EgoMed-Agent reaches 71.34\% average Dice, far above the best text-prompted baseline (11.70\%). Our code is available at \href{https://github.com/wdyyyyyy/EgoMed-Agent}{our project page}.

cs.CV↗

arXiv2026

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.

cs.LG↗

arXiv2026

Classification-Powered Conformal Inference for Zero-inflated Outcomes

Zero-inflated outcomes, where responses are zero with positive probability and otherwise continuous, are common in biomedical, environmental, and social science studies. We propose a conformal prediction based framework that provides distribution-free uncertainty quantification tailored to such outcomes. Standard conformal methods often ignore strong predictors distinguishing zero from non-zero outcomes, leading to overly conservative and unnecessarily long prediction sets. Our method integrates a classification step to identify zero outcomes and applies conformal inference to the non-zero part, producing prediction sets that are either ${0}$ or an interval. Under exchangeability, we establish that the proposed procedure attains the target marginal coverage and achieves asymptotically minimal interval length within this framework, regardless of the choice of classification or regression models. Extensive simulations and real-data application demonstrate the superior performance of our approach.

stat.ME↗

arXiv2026

Homogenization of the Stetson Photometry with the BEST Database

As one of the most widely recognized high-quality standard stars, the Stetson standards have been extensively used as a photometric reference for calibrating other surveys. In this work, we present an independent validation and re-calibration of the Stetson standard star photometry using the BEST database. Based on typically 30,000-70,000 calibration stars per band, we find that the original Stetson photometry achieves field-to-field zero-point precisions of approximately 10--40\,mmag in the $UBVRI$-band. In addition, significant spatially dependent magnitude offsets are detected within individual Stetson fields for all bands, with magnitudes exceeding 1\%, probably caused by the calibration errors in the Stetson photometry. After correcting those systematic errors, the agreement between the Stetson and BEST photometry is improved to $\sim$5\,mmag for individual fields for $BVRI$-band. The re-calibrated photometry is further validated using the SCR standards, yielding agreement better than 10\,mmag for individual stars in the $BVRI$ bands and confirming zero-point precisions of 2--4\,mmag in the $BVI$ band. The precisions is further confirmed by checks using Gaia DR3 broad band colors. These results highlight the power of the BEST database for improving photometric calibration and suggest that, if feasible, it be incorporated into the calibration process of future releases of the Stetson standard catalog.

astro-ph.SR↗

arXiv2026

Matching and mixing: Matchability of graphs under Markovian error

We consider the problem of graph matching for a sequence of graphs generated under a time-dependent Markov chain noise model. Our edgelighter error model, a variant of the classical lamplighter random walk, iteratively corrupts the graph $G_0$ with edge-dependent noise, creating a sequence of noisy graph copies $(G_t)$. Much of the graph matching literature is focused on anonymization thresholds in edge-independent noise settings, and we establish novel anonymization thresholds in this edge-dependent noise setting when matching $G_0$ and $G_t$. Moreover, we also compare this anonymization threshold with the mixing properties of the Markov chain noise model. We show that when $G_0$ is drawn from an Erd\H{o}s-R\'enyi model, the graph matching anonymization threshold and the mixing time of the edgelighter walk are both of order $\Theta(n^2\log n)$. We further demonstrate that for more structured model for $G_0$ (e.g., the Stochastic Block Model), graph matching anonymization can occur in $O(n^\alpha\log n)$ time for some $\alpha<2$, indicating that anonymization can occur before the Markov chain noise model globally mixes. Through extensive simulations, we verify our theoretical bounds in the settings of Erd\H{o}s-R\'enyi random graphs and stochastic block model random graphs, and explore our findings on real-world datasets derived from a Facebook friendship network and a European research institution email communication network.

math.ST↗

arXiv2026

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.

astro-ph.IM↗

arXiv2025

Systematic Search for Electromagnetic Counterparts to the Binary Neutron Star Merger Candidate GW231109_235456

In this letter, we present a systematic search for the electromagnetic counterparts of binary neutron star (BNS) merger candidate GW231109_235456 by examining all transients reported within the 90% probability region and detected within four days of the merger. While non-detection in $\gamma$-ray, we identify two optical candidates, each associated with and residing in a host galaxy, which locate within 330 Mpc from earth; notably, one of them, AT2023xqy, is located at a distance of $178.6$ Mpc, in good agreement with the estimated distance of the GW candidate ($\sim165^{+70}_{-69}~\mathrm{Mpc}$). Near the trigger time of GW231109_235456 (MJD 60257.996), AT2023xqy showed evidence of a $\sim$15-day rise, first detected at $3\sigma$ significance on MJD 60259.097 and confirmed above $5\sigma$ on MJD 60262.088. This was followed by a rapid $\sim$5-day decline and a plateau lasting at least 50 days, with the subsequent decay unobserved due to a data gap. The spatiotemporal coincidences indicate that AT2023xqy could be a candidate for the EM counterpart of BNS merger candidate GW231109_235456, though its lightcurve is difficult to reconcile with a standard kilonova. We examine two possible scenarios to explain the origin of AT2023xqy, a BNS merger-irrelevant scenario involving a peculiar supernova or a BNS merger-relevant scenario involving a magnetar-powered kilonova under extreme conditions. Follow-up radio observations are strongly encouraged, as they may provide critical insights into the nature of AT2023xqy.

astro-ph.HE↗

arXiv2025

Dual Attention Driven Lumbar Magnetic Resonance Image Feature Enhancement and Automatic Diagnosis of Herniation

Lumbar disc herniation (LDH) is a common musculoskeletal disease that requires magnetic resonance imaging (MRI) for effective clinical management. However, the interpretation of MRI images heavily relies on the expertise of radiologists, leading to delayed diagnosis and high costs for training physicians. Therefore, this paper proposes an innovative automated LDH classification framework. To address these key issues, the framework utilizes T1-weighted and T2-weighted MRI images from 205 people. The framework extracts clinically actionable LDH features and generates standardized diagnostic outputs by leveraging data augmentation and channel and spatial attention mechanisms. These outputs can help physicians make confident and time-effective care decisions when needed. The proposed framework achieves an area under the receiver operating characteristic curve (AUC-ROC) of 0.969 and an accuracy of 0.9486 for LDH detection. The experimental results demonstrate the performance of the proposed framework. Our framework only requires a small number of datasets for training to demonstrate high diagnostic accuracy. This is expected to be a solution to enhance the LDH detection capabilities of primary hospitals.

eess.IV↗

arXiv2025

The Mini-SiTian Array: the mini-SiTian Realtime Image Processing pipeline (STRIP)

This paper provides a comprehensive introduction to the Mini-SiTian Real-Time Image Processing pipeline (STRIP) and evaluates its operational performance. The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources. By applying the STRIP pipeline to both simulated and real observational data of the Mini-SiTian survey, it successfully identified various types of variable sources, including stellar flares, supernovae, variable stars, and asteroids, while meeting requirements of reduction speed within 5 minutes. For the real observational dataset, the pipeline detected 1 flare event, 127 variable stars, and 14 asteroids from three monitored sky regions. Additionally, two datasets were generated: one, a real-bogus training dataset comprising 218,818 training samples, and the other, a variable star light curve dataset with 421 instances. These datasets will be used to train machine learning algorithms, which are planned for future integration into STRIP.

astro-ph.IM↗

arXiv2025

The Mini-SiTian Array: White Paper

This paper outlines the scientific goals and observational strategies of the Mini-SiTian array. Mounted at Xinglong Observatory, the Mini-SiTian array consists of three 30 cm telescopes and has been in operation since 2022. The large field of view, combined with the capability for multi-band photometric observations, enables the Mini-SiTian array to perform rapid follow-up observations to identify optical counterparts of gravitational waves, capture the early light curves of tidal disruption events and supernovae, and monitor stellar flares, Be star outbursts, and cataclysmic variable stars, although its limiting magnitude is not very deep. By collaborating with the Xinglong 2.16-m telescope and leveraging a real-time image processing pipeline, simultaneous photometric and spectroscopic observations could be performed to reveal their underlying physical mechanisms. The observational and research experience provide critical guidance for the implementation of the full-scale SiTian project in the future.

astro-ph.IM↗

arXiv2025

Searching for kilonova with the SiTian prototype telescope

We simulate the optical searching of gravitational-wave electromagnetic counterpart of the binary neutron star (BNS) merger event (i.e., a kilonova) using the ground based {\it SiTian} project prototype telescope with a 5-min limiting magnitude of 22.0, triggered by LIGO and Virgo gravitational wave detectors during the ongoing O4 run. Our simulations show that an average of 0.17-0.25 kilonova events can be observed over the entire O4 period of $\sim 2$ years in the most optimistic case we set, while no kilonova can be detected in other cases. We note that it is beneficial for {\it SiTian}'s kilonova searching by extending the exposure time to gain deeper limiting magnitude despite the rapid decline of kilonova luminosity.

astro-ph.HE↗

arXiv2025

The Mini-SiTian Array: Imaging Processing Pipeline

As a pathfinder of the SiTian project, the Mini-SiTian (MST) array, employed three commercial CMOS cameras, represents a next-generation, cost-effective optical time-domain survey project. This paper focuses primarily on the precise data processing pipeline designed for wide-field, CMOS-based devices, including the removal of instrumental effects, astrometry, photometry, and flux calibration. When applying this pipeline to approximately 3000 observations taken in the Field 02 (f02) region by MST, the results demonstrate a remarkable astrometric precision of approximately 70--80\,mas (about 0.1\,pixel), an impressive calibration accuracy of approximately 1\,mmag in the MST zero points, and a photometric accuracy of about 4\,mmag for bright stars. Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs, highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys, like the SiTian project.

astro-ph.IM↗

arXiv2025

Calibration of Complementary Metal-oxide-semiconductor Sensor-based Photometry to a Few-millimagnitude Precision: The Case of the Mini-SiTian Array

We present a pioneering achievement in the high-precision photometric calibration of CMOS-based photometry, by application of the Gaia BP/RP (XP) spectra-based synthetic photometry (XPSP) method to the mini-SiTian array (MST) photometry. Through 79 repeated observations of the $\texttt{f02}$ field on the night, we find good internal consistency in the calibrated MST $G_{\rm MST}$-band magnitudes for relatively bright stars, with a precision of about 4\,mmag for $G_{\rm MST}\sim 13$. Results from more than 30 different nights (over 3100 observations) further confirm this internal consistency, indicating that the 4\,mmag precision is stable and achievable over timescales of months. An independent external validation using spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR10 and high-precision photometric data using CCDs from Gaia DR3 reveals a zero-point consistency better than 1\,mmag. Our results clearly demonstrate that CMOS photometry is on par with CCD photometry for high-precision results, highlighting the significant capabilities of CMOS cameras in astronomical observations, especially for large-scale telescope survey arrays.

astro-ph.IM↗

arXiv2025

"Frog-eyes" in Astronomy: Monitoring Binary Radial Velocity Variations Through A Pair of Narrow-Band Filters

Spectroscopic observations are a crucial step in driving major discoveries in the era of time-domain surveys. However, the pace of current spectroscopic surveys is increasingly unable to meet the demands of rapidly advancing large-scale time-domain surveys. To address this issue, we propose the ``Frog-eyes" system, which employs a pair of narrow-band filters: one positioned near a strong absorption line to capture signals from Doppler shifts, and the other placed on the adjacent continuum to monitor intrinsic variations. The combination of observations from the two filters enables the extraction of radial velocity (RV) curves from a large sample of binary stars, and is particularly efficient for single-lined binaries (SB1), using photometric techniques. Comprehensive mock simulations on SB1 demonstrate that the binary orbital parameters can be precisely measured from the extracted RV curves for binary systems where the primary star has an effective temperature greater than 6000 K. With a typical ground-based photometric precision of approximately 0.3%, the uncertainties in the derived semi-amplitude K and eccentricity e are less than 10% and 0.1, respectively, for binary systems with K $\ge$ 30 km/s. These encouraging results are further validated by real observations of the hot subdwarf-white dwarf binary system HD 265435, using a non-specialized ``Frog-eyes" system installed on the Chinese 2.16m telescope. Once this system is properly installed on large-field-of-view survey telescopes, the rate of acquiring RV curves for binaries will approach their detection rate in leading time-domain photometric surveys.

astro-ph.SR↗

arXiv2024

Photometric Stellar Parameters for 195,478 Kepler Input Catalog (KIC) Stars

The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the past decades, accurate spectroscopic estimates of these parameters are available for only about half of the stars in the full KIC catalog. In our work, by training relationships between photometric colors and spectroscopic stellar parameters from Gaia DR3, the Kepler Issac-Newton Survey, LAMOST DR10, and APOGEE DR17, we have obtained atmospheric-parameter estimates for over 195,000 stars, accounting for 97$\%$ of the total sample of KIC stars. We obtain 1$\sigma$ uncertainties of 0.1 dex on metallicity [Fe/H], 100 K on effective temperature $T_{\rm eff}$, and 0.2 dex on surface gravity log $g$. In addition, based on these atmospheric parameters, we estimated the ages, masses, radii, and surface gravities of these stars using the commonly adopted isochrone-fitting approach. External comparisons indicate that the resulting precision for turn-off stars is 20$\%$ in age; for dwarf stars, it is 0.07 $M_{\odot}$ in mass, 0.05 $R_{\odot}$ in radius, and 0.12 dex in surface gravity; and for giant stars, it is 0.14 $M_{\odot}$ in mass, 0.73 $R_{\odot}$ in radius, and 0.11 dex in surface gravity.

astro-ph.SR↗

arXiv2024

Gotta match 'em all: Solution diversification in graph matching matched filters

We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph. Our method builds upon the graph-matching-matched-filter technique proposed in Sussman et al., with the discovery of multiple diverse matchings being achieved by iteratively penalizing a suitable node-pair similarity matrix in the matched filter algorithm. In addition, we propose algorithmic speed-ups that greatly enhance the scalability of our matched-filter approach. We present theoretical justification of our methodology in the setting of correlated Erdos-Renyi graphs, showing its ability to sequentially discover multiple templates under mild model conditions. We additionally demonstrate our method's utility via extensive experiments both using simulated models and real-world dataset, include human brain connectomes and a large transactional knowledge base.

stat.ML↗

arXiv2024

Filter Design for Estimation of Stellar Metallicity: Insights from Experiments with Gaia XP Spectra

We search for an optimal filter design for the estimation of stellar metallicity, based on synthetic photometry from Gaia XP spectra convolved with a series of filter-transmission curves defined by different central wavelengths and bandwidths. Unlike previous designs based solely on maximizing metallicity sensitivity, we find that the optimal solution provides a balance between the sensitivity and uncertainty of the spectra. With this optimal filter design, the best precision of metallicity estimates for relatively bright ($G \sim 11.5$) stars is excellent, $σ_{\rm [Fe/H]} = 0.034$\,dex for FGK dwarf stars, superior to that obtained utilizing custom sensitivity-optimized filters (e.g., SkyMapper\,$v$). By selecting hundreds of high-probabability member stars of the open cluster M67, our analysis reveals that the intrinsic photometric-metallicity scatter of these cluster members is only 0.036\,dex, consistent with this level of precision. Our results clearly demonstrate that the internal precision of photometric-metallicity estimates can be extremely high, even providing the opportunity to perform chemical tagging for very large numbers of field stars in the Milky Way. This experiment shows that it is crucial to take into account uncertainty alongside the sensitivity when designing filters for measuring the stellar metallicity and other parameters.

astro-ph.SR↗

arXiv2023

Clustered Graph Matching for Label Recovery and Graph Classification

Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network. We consider matching the shuffled network to averages of the networks in the vertex-aligned collection at different levels of granularity. We demonstrate both in theory and practice that if the graphs come from different network classes, then clustering the networks into classes followed by matching the new graph to cluster-averages can yield higher fidelity matching performance than matching to the global average graph. Moreover, by minimizing the graph matching objective function with respect to each cluster average, this approach simultaneously classifies and recovers the vertex labels for the shuffled graph. These theoretical developments are further reinforced via an illuminating real data experiment matching human connectomes.

stat.ML↗