arXiv subjects
Publications and source records attributed to Mengyao Wu.
We show that in any $2n+2$ dimensions, the higher Chern-Simons action built from a semistrict Lie 2-algebra gives a non-trivial higher Wess-Zumino-Witten (WZW) term under a higher gauge transformation. The key point is that the non-zero Jacobiator directly generates the higher WZW term, whereas it vanishes when reduced to the strict case.
In this paper, we give a compact formulation of strict higher gauge theory based on generalized (differential) forms that package fields of multiple form degrees into a single variable. We define generalized forms valued in higher algebras and higher groups and derive the corresponding Maurer--Cartan structures. This leads to uniform, gauge-theory-like expressions for higher connections, curvatures, Bianchi identities, and gauge transformations. We further construct action principles for higher Chern--Simons and higher Yang--Mills theories within the same formalism and compute the associated topological densities in the corresponding dimensions.
In this paper, we develop the higher descent equations for higher gauge theories within the framework of 2-term $L_{\infty}$ algebras. Starting from a multilinear symmetric invariant polynomial, we construct a family of higher Chern-Simons type characteristic classes and verify that they satisfy the higher descent equations. These polynomials encode both the higher Chern-Weil theorem and the higher gauge anomalies.
Vision Foundation Models (VFMs) excel in generalization due to large-scale pretraining, but fine-tuning them for Domain Generalized Semantic Segmentation (DGSS) while maintaining this ability remains challenging. Existing approaches either selectively fine-tune parameters or freeze the VFMs and update only the adapters, both of which may underutilize the VFMs' full potential in DGSS tasks. We observe that domain-sensitive parameters in VFMs, arising from task and distribution differences, can hinder generalization. To address this, we propose \textbf{FisherTune}, a robust fine-tuning method guided by the Domain-Related Fisher Information Matrix (DR-FIM). DR-FIM measures parameter sensitivity across tasks and domains, enabling selective updates that preserve generalization and enhance DGSS adaptability. FisherTune incorporates variational inference to stabilize DR-FIM estimation, treating parameters as Gaussian-distributed variables and leveraging pre-trained priors. Extensive experiments show that FisherTune achieves superior cross-domain segmentation while maintaining generalization, outperforming selective-parameter and adapter-based methods.
In this paper,we extend the definition of the Chern-Simons type characteristic classes in the continuous case to abelian lattice gauge theory. Then, we show that the exterior differential of a k-th Chern-Simons type characteristic class is exactly equal to the coboundary of the cochain of the (k-1)-th Chern-Simons type characteristic classes based upon the noncommutative differential calculus on the lattice.
We present higher Chern-Simons theories based on (2-)crossed modules. We start from the generalized differential forms in Generalized Differential Calculus and define the corresponding generalized connections which consist of higher connections. Then we establish the generalized Chern-Simons forms to get the higher Chern-Simons actions. Finally, we develop the higher second Chern forms and Chern-Weil theorems.