Navigating the digital spectrum: Assessing political bias, stability, and downstream fairness in Large Language Models
研究通过引入稳健的政治罗盘测试框架，评估大型语言模型的政治倾向、稳定性和下游公平性问题，揭示了指令表述、语言和回答格式等因素对结果的影响。
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Selected work
Representative Papers
Physics-informed distribution of relaxation times estimation and latent-space condition monitoring of solid oxide fuel and electrolysis cells from electrochemical impedance spectroscopy
Estimating the distribution of relaxation times (DRT) from electrochemical impedance spectroscopy (EIS) data constitutes an ill-posed inverse problem highly sensitive to regularization. This work proposes a physics-informed convolutional autoencoder that directly embeds the discretized EIS–DRT physical relationship into the training process, enabling consistent DRT reconstruction across datasets without per-spectrum hyperparameter tuning. The model demonstrates excellent performance on both synthetic double-ZARC spectra and three independent experimental datasets from solid oxide cells, achieving normalized reconstruction errors below 1.1%. Furthermore, the latent space is found to be naturally organized by relaxation time scales, facilitating interpretable operational monitoring and effectively capturing dynamic changes, hydrogen starvation events, and long-term degradation.
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Performance Trade-offs of High Order Meshless Approximation on Distributed Memory Systems
High-order radial basis function-generated finite differences (RBF-FD) methods for solving the Poisson equation on distributed-memory systems face an inherent trade-off between numerical accuracy and communication overhead. Method: This work systematically quantifies how approximation order affects compute-communication efficiency, and proposes a target-accuracy-driven adaptive order selection strategy. The approach integrates RBF-FD derivative approximation, OpenMPI-based inter-node communication, and OpenMP-based intra-node parallelism, implemented atop an explicit iterative solver on CPU clusters. Contribution/Results: We establish, for the first time, a quantitative relationship between problem size and optimal approximation order. Our strategy significantly reduces total time-to-solution for prescribed accuracy—achieving 1.8×–2.5× speedup over fixed-order baselines in representative benchmarks. The work delivers a reusable performance modeling and tuning paradigm for deploying high-order meshfree methods in distributed environments.
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Recent publications
Latest Papers
Navigating the digital spectrum: Assessing political bias, stability, and downstream fairness in Large Language Models
研究通过引入稳健的政治罗盘测试框架，评估大型语言模型的政治倾向、稳定性和下游公平性问题，揭示了指令表述、语言和回答格式等因素对结果的影响。
0 citationsRead paper
Physics-informed distribution of relaxation times estimation and latent-space condition monitoring of solid oxide fuel and electrolysis cells from electrochemical impedance spectroscopy
Estimating the distribution of relaxation times (DRT) from electrochemical impedance spectroscopy (EIS) data constitutes an ill-posed inverse problem highly sensitive to regularization. This work proposes a physics-informed convolutional autoencoder that directly embeds the discretized EIS–DRT physical relationship into the training process, enabling consistent DRT reconstruction across datasets without per-spectrum hyperparameter tuning. The model demonstrates excellent performance on both synthetic double-ZARC spectra and three independent experimental datasets from solid oxide cells, achieving normalized reconstruction errors below 1.1%. Furthermore, the latent space is found to be naturally organized by relaxation time scales, facilitating interpretable operational monitoring and effectively capturing dynamic changes, hydrogen starvation events, and long-term degradation.
0 citationsRead paper
Performance Trade-offs of High Order Meshless Approximation on Distributed Memory Systems
High-order radial basis function-generated finite differences (RBF-FD) methods for solving the Poisson equation on distributed-memory systems face an inherent trade-off between numerical accuracy and communication overhead. Method: This work systematically quantifies how approximation order affects compute-communication efficiency, and proposes a target-accuracy-driven adaptive order selection strategy. The approach integrates RBF-FD derivative approximation, OpenMPI-based inter-node communication, and OpenMP-based intra-node parallelism, implemented atop an explicit iterative solver on CPU clusters. Contribution/Results: We establish, for the first time, a quantitative relationship between problem size and optimal approximation order. Our strategy significantly reduces total time-to-solution for prescribed accuracy—achieving 1.8×–2.5× speedup over fixed-order baselines in representative benchmarks. The work delivers a reusable performance modeling and tuning paradigm for deploying high-order meshfree methods in distributed environments.
0 citationsRead paper