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Lina Zhang

Lina Zhang contributes to research discovery and scholarly infrastructure.

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Computer Vision econ.EM Artificial Intelligence econ.GN gr-qc q-fin.EC

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preprint2026arXiv

Can Multimodal Large Language Models Understand Pathologic Movements? A Pilot Study on Seizure Semiology

Multimodal Large Language Models (MLLMs) have demonstrated robust capabilities in recognizing everyday human activities, yet their potential for analyzing clinically significant involuntary movements in neurological disorders remains largely unexplored. This pilot study evaluates the capability of MLLMs for automated recognition of pathological movements in seizure videos. We assessed the zero-shot performance of state-of-the-art MLLMs on 20 ILAE-defined semiological features across 90 clinical seizure recordings. MLLMs outperformed fine-tuned Convolutional Neural Network (CNN) and Vision Transformer (ViT) baseline models on 13 of 18 features without task-specific training, demonstrating particular strength in recognizing salient postural and contextual features while struggling with subtle, high-frequency movements. Feature-targeted signal enhancement (facial cropping, pose estimation, audio denoising) improved performance on 10 of 20 features. Expert evaluation showed that 94.3 percent of MLLM-generated explanations for correctly predicted cases achieved at least 60 percent faithfulness scores, aligning with epileptologist reasoning. These findings demonstrate the potential of adapting general-purpose MLLMs for specialized clinical video analysis through targeted preprocessing strategies, offering a path toward interpretable, efficient diagnostic assistance. Our code is publicly available at https://github.com/LinaZhangUCLA/PathMotionMLLM.

preprint2025arXiv

Newly scalarization of the Einstein-Euler-Heisenberg black hole

Th spontaneous scalarization of the Einstein-Euler-Heisenberg (EEH) black hole is performed in the EEH-scalar theory by introducing an exponential scalar coupling (with $α$ coupling constant) to the Maxwell term.Here, the EEH black hole as a blad black hole is described by mass $M$ and magnetic charge $q$ with an action parameter $μ$. A choice of $μ=0.3$ gurantees a single horizon with unrestricted magnetic charge $q$. The onset scalarization of this black hole appears for a positive coupling $α$ with an unlimited magnetic charge $q$. However, there exists a difference between $q\le1$ and $q>1$ onset scalarizations. We notify the presence of infinite branches labeled by the number of $n=0,1,2,\cdots$ of scalarized charged black holes by taking into account the scalar seeds around the EEH black hole. We find that the $n=0$ fundamental branch of all scalarized black holes is stable against the radial perturbations, while the $n=1$ excited branch is unstable.

preprint2022arXiv

Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects

This paper examines the identification power of instrumental variables (IVs) for average treatment effect (ATE) in partially identified models. We decompose the ATE identification gains into components of contributions driven by IV relevancy, IV strength, direction and degree of treatment endogeneity, and matching via exogenous covariates. Our decomposition is demonstrated with graphical illustrations, simulation studies and an empirical example of childbearing and women's labour supply. Our analysis offers insights for understanding the complex role of IVs in ATE identification and for selecting IVs in practical policy designs. Simulations also suggest potential uses of our analysis for detecting irrelevant instruments.

preprint2021arXiv

Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images

Recently, flow-based methods have achieved promising success in video frame interpolation. However, electron microscopic (EM) images suffer from unstable image quality, low PSNR, and disorderly deformation. Existing flow-based interpolation methods cannot precisely compute optical flow for EM images since only predicting each position's unique offset. To overcome these problems, we propose a novel interpolation framework for EM images that progressively synthesizes interpolated features in a coarse-to-fine manner. First, we extract missing intermediate features by the proposed temporal spatial-adaptive (TSA) interpolation module. The TSA interpolation module aggregates temporal contexts and then adaptively samples the spatial-related features with the proposed residual spatial adaptive block. Second, we introduce a stacked deformable refinement block (SDRB) further enhance the reconstruction quality, which is aware of the matching positions and relevant features from input frames with the feedback mechanism. Experimental results demonstrate the superior performance of our approach compared to previous works, both quantitatively and qualitatively.

preprint2021arXiv

Weak Identification in Discrete Choice Models

We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that when the null hypothesis of weak identification is rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo study compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of using conventional weak identification tests for linear models in the discrete choice model context. Furthermore, we compare our approach against those commonly applied in the literature in two empirical examples: married women labor force participation, and US food aid and civil conflicts.

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