arXiv subjects

Xihan Zhang

Publications and source records attributed to Xihan Zhang.

4 recordsLinked to original sources

arXiv2026

Estimating the Containment Effectiveness and Economic Cost of Inner-city Non-Pharmaceutical Interventions

Non-pharmaceutical interventions (NPIs) are crucial for controlling pandemics, but existing research often overlooks the heterogeneity of individual behavior, which can lead to inaccurate evaluations of the effectiveness of strategies. In this paper, we use a large dataset of fine-grained real-world individual trajectory data from a major Chinese city to examine the trade-off between the epidemic containment effectiveness and economic cost of different NPIs. Our findings reveal significant variations in the outcomes of different NPIs across activation mechanisms and initial scales of undetected transmission. Based on these results, we construct a two-dimensional evaluation framework that comprehensively evaluates the impact of both the containment effectiveness and economic cost, which suggests that implementing stringent strategies-such as lockdown or contact tracing-at low activation thresholds can achieve optimal epidemic control with minimal economic cost. Our study provides a data-driven decision-making framework for understanding the implementation effectiveness and applicability of emergency management policies within urban systems.

physics.soc-ph↗

arXiv2025

Isolating Balanced Ocean Dynamics in SWOT Data

The Surface Water and Ocean Topography (SWOT) mission provides two-dimensional sea surface height (SSH) maps at unprecedented resolution, but its signal is a combination of balanced meso- and submesoscale turbulence, unbalanced internal waves, and small-scale noise. Interpreting the meso- and submesoscale flow features captured by SWOT requires a careful isolation of the balanced signal. We present a statistical method to do so in regions where internal-wave signals are negligible, such as western boundary current regions and the Southern Ocean. Our method assumes Gaussian statistics for both the balanced flow and the noise, which we infer by fitting parametric models to the observed SSH wavenumber spectrum. Using these inferred parameters, we perform a Bayesian inversion to reconstruct swath-aligned SSH maps that fill the nadir gap. We evaluate the method using synthetic data from a high-resolution simulation with realistic SWOT-like noise added. Comparisons with the underlying model data show that our reconstruction successfully removes small-scale noise while preserving meso- and submesoscale eddies, fronts, and filaments down to a feature scale of 10km. The comparison also demonstrates that the posterior uncertainty is a reliable estimate of the error.

physics.ao-ph↗

arXiv2025

Assessing submesoscale sea surface height signals from the SWOT mission

The sea surface height (SSH) field measured by Surface Water and Ocean Topography (SWOT) mission's wide-swath altimeter is analyzed with a focus on submesoscale features. Along-track wavenumber spectra of SSH variance are estimated for the global ocean using the 1-day repeat period from March 26 to July 10, 2023. In regions with an energetic mesoscale eddy field, the spectra have a mesoscale plateau, a steep drop-off due to balanced submesoscale turbulence, and a much flatter power-law tail at small scales. These spectra are characterized by fitting a spectral model. For the balanced signal, this fit yields a power law exponent between -4 and -6 for most regions, broadly consistent with expectations and previous observations. The amplitude of the distinct small-scale signal, which typically dominates at wavelengths less than 30 to 50 km, is strongly correlated in time and space with the height of surface gravity waves, suggesting aliased wave signals as the most likely source. A simple method is proposed to isolate the balanced signal in regions with negligible internal tides. Maps of the balanced signal in the Antarctic Circumpolar Current show compact cyclones with geostrophic relative vorticities frequently in excess of the local planetary vorticity, challenging the quasi-geostrophic framework commonly used to interpret altimetric data.

physics.ao-ph↗

arXiv2025

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models

Large Language Models (LLMs) are increasingly used in high-stakes fields where their decisions impact rights and equity. However, LLMs' judicial fairness and implications for social justice remain underexplored. When LLMs act as judges, the ability to fairly resolve judicial issues is a prerequisite to ensure their trustworthiness. Based on theories of judicial fairness, we construct a comprehensive framework to measure LLM fairness, leading to a selection of 65 labels and 161 corresponding values. Applying this framework to the judicial system, we compile an extensive dataset, JudiFair, comprising 177,100 unique case facts. To achieve robust statistical inference, we develop three evaluation metrics, inconsistency, bias, and imbalanced inaccuracy, and introduce a method to assess the overall fairness of multiple LLMs across various labels. Through experiments with 16 LLMs, we uncover pervasive inconsistency, bias, and imbalanced inaccuracy across models, underscoring severe LLM judicial unfairness. Particularly, LLMs display notably more pronounced biases on demographic labels, with slightly less bias on substance labels compared to procedure ones. Interestingly, increased inconsistency correlates with reduced biases, but more accurate predictions exacerbate biases. While we find that adjusting the temperature parameter can influence LLM fairness, model size, release date, and country of origin do not exhibit significant effects on judicial fairness. Accordingly, we introduce a publicly available toolkit containing all datasets and code, designed to support future research in evaluating and improving LLM fairness.

cs.CL↗