arXiv subjects

Zheng Guan

Publications and source records attributed to Zheng Guan.

4 recordsLinked to original sources

arXiv2026

ZStar: An automated toolkit for polarization, Born effective charges, dielectric response, and infrared and Raman spectra calculations

Born effective charges (BEC) link atomic displacements to polarization, connecting lattice vibrations to dielectric response and infrared activity. We present ZStar, an open-source Python toolkit that automates calculations of polarization, BEC, phonons, static and frequency-dependent dielectric response, and infrared and Raman spectra. ZStar supports calculations across all dimensionalities, including bulk crystals, two-dimensional materials, one-dimensional structures, and molecules, through an integrated workflow spanning input preparation, execution, and response analysis. Berry-phase polarization and real-space dipoles describe responses along periodic and open directions, respectively. Our unified, symmetry-adapted finite-displacement framework jointly determines BEC and zone-center force constants from the same calculations, improving computational efficiency while preserving numerical accuracy. Reusing the electronic Hamiltonian in the ABACUS+PYATB route enables Raman calculations without additional self-consistent calculations. Reproducible examples and a packaged agent skill support conventional and agent-assisted use. ZStar enables efficient, automated exploration of polarization, BEC, dielectric response, and vibrational spectra across all dimensionalities.

cond-mat.mtrl-sci↗

arXiv2026

Multi-Modal Image Fusion via Intervention-Stable Feature Learning

Multi-modal image fusion integrates complementary information from different modalities into a unified representation. Current methods predominantly optimize statistical correlations between modalities, often capturing dataset-induced spurious associations that degrade under distribution shifts. In this paper, we propose an intervention-based framework inspired by causal principles to identify robust cross-modal dependencies. Drawing insights from Pearl's causal hierarchy, we design three principled intervention strategies to probe different aspects of modal relationships: i) complementary masking with spatially disjoint perturbations tests whether modalities can genuinely compensate for each other's missing information, ii) random masking of identical regions identifies feature subsets that remain informative under partial observability, and iii) modality dropout evaluates the irreplaceable contribution of each modality. Based on these interventions, we introduce a Causal Feature Integrator (CFI) that learns to identify and prioritize intervention-stable features maintaining importance across different perturbation patterns through adaptive invariance gating, thereby capturing robust modal dependencies rather than spurious correlations. Extensive experiments demonstrate that our method achieves SOTA performance on both public benchmarks and downstream high-level vision tasks.

cs.CV↗

arXiv2025

Adaptive Knowledge Transfer for Cross-Disciplinary Cold-Start Knowledge Tracing

Cross-Disciplinary Cold-start Knowledge Tracing (CDCKT) faces a critical challenge: insufficient student interaction data in the target discipline prevents effective knowledge state modeling and performance prediction. Existing cross-disciplinary methods rely on overlapping entities between disciplines for knowledge transfer through simple mapping functions, but suffer from two key limitations: (1) overlapping entities are scarce in real-world scenarios, and (2) simple mappings inadequately capture cross-disciplinary knowledge complexity. To overcome these challenges, we propose Mixed of Experts and Adversarial Generative Network-based Cross-disciplinary Cold-start Knowledge Tracing Framework. Our approach consists of three key components: First, we pre-train a source discipline model and cluster student knowledge states into K categories. Second, these cluster attributes guide a mixture-of-experts network through a gating mechanism, serving as a cross-domain mapping bridge. Third, an adversarial discriminator enforces feature separation by pulling same-attribute student features closer while pushing different-attribute features apart, effectively mitigating small-sample limitations. We validate our method's effectiveness across 20 extreme cross-disciplinary cold-start scenarios.

cs.IR↗

arXiv2023

Human Pose Transfer with Augmented Disentangled Feature Consistency

Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with augmented Disentangled Feature Consistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates a keypoint amplifier to enhance the pose feature extraction. With the help of the disentangled feature consistency losses, we further propose a novel data augmentation scheme that introduces unpaired support data with the augmented consistency constraints to improve the generality and robustness of DFC-Net. Extensive experimental results on Mixamo-Pose and EDN-10k have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.

cs.CV↗