arXiv subjects

Anqing Chen

Publications and source records attributed to Anqing Chen.

4 recordsLinked to original sources

arXiv2026

RepliCore: Reproducible Parallel Simulation under Asynchronous Browser Runtimes

Browser-based simulations execute over asynchronous runtime mechanisms including event loops, rendering callbacks, and independently scheduled Web Workers, causing simulation progression to depend on runtime timing and callback scheduling behavior. RepliCore addresses this problem by separating asynchronous runtime progression from externally observable logical-state visibility. Runtime-visible simulation states are exposed only after logical-state progression becomes externally stable, preventing asynchronous runtime activities from observing partially updated simulation progression. The framework prevents rendering callbacks and asynchronous runtime tasks from observing transient intermediate logical states during parallel progression. This organization maintains consistency between parallel and sequential execution. Based on this model, we implement RepliCore, a browser-oriented deterministic parallel simulation framework for reproducible large-scale simulation under asynchronous browser runtimes. Experiments in real browser environments produce bitwise-identical outputs across varying worker configurations, scheduling conditions, and rendering frequencies while remaining practical for large-scale browser-oriented workloads. Additional ablation experiments show systematic state divergence after relaxing key execution constraints. These results indicate that reproducible asynchronous parallel simulation can be achieved through controlled logical-state visibility stabilization without relying on execution replay or explicit schedule control.

cs.DC↗

arXiv2023

Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation

Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders -- decision makers (faculty) and decision subjects (students) -- use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making.

cs.HC↗

arXiv2023

Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation

Hyper-partisan misinformation has become a major public concern. In order to examine what type of misinformation label can mitigate hyper-partisan misinformation sharing on social media, we conducted a 4 (label type: algorithm, community, third-party fact-checker, and no label) X 2 (post ideology: liberal vs. conservative) between-subjects online experiment (N = 1,677) in the context of COVID-19 health information. The results suggest that for liberal users, all labels reduced the perceived accuracy and believability of fake posts regardless of the posts' ideology. In contrast, for conservative users, the efficacy of the labels depended on whether the posts were ideologically consistent: algorithmic labels were more effective in reducing the perceived accuracy and believability of fake conservative posts compared to community labels, whereas all labels were effective in reducing their belief in liberal posts. Our results shed light on the differing effects of various misinformation labels dependent on people's political ideology.

cs.HC↗

arXiv2020

Deconvolution optical-resolution photoacoustic microscope for high -resolution imaging of brain

We proposed a deconvolution optical-resolution photoacoustic microscope for high -resolution imaging of brain. The focal spot of the photoacoustic microscopy is measured to obtain the lateral PSF (point spread function) of the system. Making the measured PSF as the initial system PSF to perform Lucy- Richardson (LR) deconvolution. The image resolution of cerebral vasculature obtained by this method is higher. The full width at half maximum (FWHM) of the width of blood vessel before and after deconvolution are 7 micrometers and 3.6 micrometers, respectively, and the image definition is increased by about 1.9 times. Experiments show that this method can further improve the clarity of photoacoustic images of cerebral vasculature, which lays the foundation for further research on brain imaging.

physics.med-ph↗