Local Laplacian: theory and models for data analysis

This paper introduces the persistent local Laplacian formalism, a theoretically grounded and highly parallelizable framework that overcomes the sensitivity and scalability limitations of traditional topological data analysis by establishing a generalized persistent Hodge isomorphism and unitary equivalence to efficiently extract fine-grained local structural signatures from large-scale datasets.

Jian Liu, Hongsong Feng, Kefeng Liu2026-03-10🔢 math

Proceedings Eighth International Conference on Applied Category Theory

This paper presents the proceedings of the Eighth International Conference on Applied Category Theory (ACT2025), held at the University of Florida in June 2025, which featured a diverse collection of contributions spanning pure and applied disciplines such as computer science, quantum computation, and chemistry.

Amar Hadzihasanovic (Tallinn University of Technology), Jean-Simon Pacaud Lemay (Macquarie University)2026-03-10🔢 math

Existence, Sharp Boundary Asymptotics, and Stochastic Optimal Control for Semilinear Elliptic Equations with Gradient-Dependent Terms and Singular Weights

This paper establishes the existence, uniqueness, and sharp boundary asymptotics of large solutions to semilinear elliptic equations with gradient-dependent terms and singular weights, while also proving their strict convexity and identifying them as value functions for infinite-horizon stochastic optimal control problems.

Dragos-Patru Covei2026-03-10🔢 math

Extreme value theorem for geodesic flow on the quotient of the theta group

This paper establishes an extreme value theorem for the geodesic flow on the hyperbolic surface associated with the theta group by introducing a spliced continued fraction algorithm, proving its dynamical equivalence to the flow's first return map, and deriving a Galambos-type extreme value law for maximal cusp excursions via spectral analysis of the transfer operator.

Jaelin Kim, Seul Bee Lee, Seonhee Lim2026-03-10🔢 math

Group-Sparse Smoothing for Longitudinal Models with Time-Varying Coefficients

This paper proposes TV-Select, a unified framework that simultaneously identifies relevant variables and distinguishes between constant and time-varying effects in longitudinal models by employing a doubly penalized B-spline approach with group Lasso and roughness penalties to achieve accurate structural recovery, smooth estimation, and improved predictive performance.

Yu Lu, Tianni Zhang, Yuyao Wang, Mengfei Ran2026-03-10🔢 math

Rough differential equations driven by TFBM with Hurst index H(14,13)H\in (\frac{1}{4}, \frac{1}{3})

This paper establishes the existence and uniqueness of solutions to rough differential equations driven by tempered fractional Brownian motion with Hurst index H(14,13)H \in (\frac{1}{4}, \frac{1}{3}) by canonically lifting the noise to a geometric rough path and employing a Doss-Sussmann transformation combined with a greedy stopping time sequence, while also deriving quantitative growth bounds for the solutions.

Lijuan Zhang, Jianhua Huang2026-03-10🔢 math

Heterogeneous Stochastic Momentum ADMM for Distributed Nonconvex Composite Optimization

This paper proposes HSM-ADMM, a novel distributed stochastic algorithm for nonconvex composite optimization that achieves optimal O(ϵ1.5)\mathcal{O}(\epsilon^{-1.5}) complexity with a single-loop structure and minimal communication by employing node-specific adaptive step sizes to decouple convergence stability from global network properties.

Yangming Zhang, Yongyang Xiong, Jinming Xu, Keyou You, Yang Shi2026-03-10🔢 math