Fuwei Liu | Searcharxiv

arXiv2026

Freeze-in Warm Dark Matter via Dimension-6 Operators in 3-3-1 Models

We propose a natural resolution to the fine-tuning problem inherent in the freeze-in dark matter paradigm by embedding a sterile singlet within a 3-3-1 electroweak extension. By imposing an exact $Z_{13}$ discrete gauge symmetry, we formally suppress all low-dimensional portals to ensure that the dark sector communicates with the Standard Model (SM) exclusively through a dimension-six operator. This theoretical structure allows the extraordinarily small coupling required for dark matter production to emerge naturally from the profound hierarchy between the electroweak scale and the ultra-high Peccei-Quinn symmetry breaking scale. Detailed numerical integration of the Boltzmann equations demonstrates that the sterile singlet can be produced via the infrared freeze-in mechanism to match the observed relic abundance of $\Omega_S h^2 = 0.12$. The resulting keV-scale warm dark matter candidate remains consistent with stringent Lyman-alpha forest constraints while offering a viable solution to galactic-scale discrepancies such as the cusp-core and missing satellites problems. Ultimately, this framework provides a self-consistent unification of dark matter genesis and the strong CP solution that is completely independent of ad hoc parameter adjustments.

hep-ph↗

arXiv2025

ICAS: IP Adapter and ControlNet-based Attention Structure for Multi-Subject Style Transfer Optimization

Generating multi-subject stylized images remains a significant challenge due to the ambiguity in defining style attributes (e.g., color, texture, atmosphere, and structure) and the difficulty in consistently applying them across multiple subjects. Although recent diffusion-based text-to-image models have achieved remarkable progress, existing methods typically rely on computationally expensive inversion procedures or large-scale stylized datasets. Moreover, these methods often struggle with maintaining multi-subject semantic fidelity and are limited by high inference costs. To address these limitations, we propose ICAS (IP-Adapter and ControlNet-based Attention Structure), a novel framework for efficient and controllable multi-subject style transfer. Instead of full-model tuning, ICAS adaptively fine-tunes only the content injection branch of a pre-trained diffusion model, thereby preserving identity-specific semantics while enhancing style controllability. By combining IP-Adapter for adaptive style injection with ControlNet for structural conditioning, our framework ensures faithful global layout preservation alongside accurate local style synthesis. Furthermore, ICAS introduces a cyclic multi-subject content embedding mechanism, which enables effective style transfer under limited-data settings without the need for extensive stylized corpora. Extensive experiments show that ICAS achieves superior performance in structure preservation, style consistency, and inference efficiency, establishing a new paradigm for multi-subject style transfer in real-world applications.

cs.CV↗