惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

A
About on SuperTechFans
G
Google Developers Blog
L
LangChain Blog
aimingoo的专栏
aimingoo的专栏
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
云风的 BLOG
云风的 BLOG
小众软件
小众软件
月光博客
月光博客
Recent Announcements
Recent Announcements
人人都是产品经理
人人都是产品经理
P
Proofpoint News Feed
博客园 - 聂微东
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
雷峰网
雷峰网
The Cloudflare Blog
博客园_首页
美团技术团队
大猫的无限游戏
大猫的无限游戏
B
Blog
IT之家
IT之家
Jina AI
Jina AI
H
Hackread – Cybersecurity News, Data Breaches, AI and More
C
Check Point Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
Exact Fourier dimensions of dyadic Mandelbrot cascades un...
Yin Cai, Guozheng Cheng, Xiang Fang, Menghan Li, Hongdou Qu, Che · 2026-06-07 · via math updates on arXiv.org

We determine the Fourier dimension of dyadic Mandelbrot cascades under the minimal Kahane-Peyriere integrability condition. The interval theorem is proved in a vector-valued dyadic cascade model in which sibling weights may have arbitrary dependence. For every balanced energy-admissible vector law, almost surely on non-extinction, dim_F(mu)=dim_E(mu)=dim_2(mu)=D_E(X). In the canonical scalar case, under W>=0, E W=1, E[W log_2^+ W]<infinity, and E[W log_2 W]<1, the formula becomes dim_F(mu)=dim_E(mu)=dim_2(mu)=sup_{1<q<2} max{0, 2-(2/q)(1+log_2 E[W^q])}, with the convention that the corresponding term is zero when E[W^q]=infinity. In particular, this scalar specialization gives the canonical Mandelbrot-Kahane Fourier-dimension formula under the minimal integrability condition. We also prove the endpoint theorem for the dyadic Mandelbrot cascade on the unit circle. Under the same scalar assumption, almost surely on non-extinction, dim_F(mu_circle)=sup_{q>1} max{0, (q-1-log_2 E[W^q])/q}. The interval and circle formulas share a light-tail/heavy-tail dichotomy but have different mechanisms: energy dimension for the interval, and minimum lower local dimension for the circle. The circle lower bound follows from a finite-moment annular Fourier theorem.