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

V
Visual Studio Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
G
Google Developers Blog
J
Java Code Geeks
爱范儿
爱范儿
Microsoft Azure Blog
Microsoft Azure Blog
美团技术团队
人人都是产品经理
人人都是产品经理
Martin Fowler
Martin Fowler
IT之家
IT之家
博客园_首页
B
Blog RSS Feed
Google DeepMind News
Google DeepMind News
B
Blog
U
Unit 42
Apple Machine Learning Research
Apple Machine Learning Research
L
LangChain Blog
Stack Overflow Blog
Stack Overflow Blog
罗磊的独立博客
N
Netflix TechBlog - Medium
T
Tailwind CSS Blog
博客园 - 聂微东
腾讯CDC
A
About on SuperTechFans

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
Proof of the Gawron-Miska-Ulas conjecture concerning unbo...
[Submitted on 24 Jun 2026] · 2026-06-25 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:It is well known that $F(x)=\prod_{n=0}^{\infty}(1-x^{2^n})$ is the generating function of the Prouhet-Thue-Morse sequence $\{(-1)^{\sigma_2(n)}\}_{n=0}^\infty$, where $\sigma_2(n)$ is the sum of (binary) digits of $n$. Let $m$ be an integer. In 2018, Gawron, Miska and Ulas initiated the study of arithmetic properties of power series expansion of the function $$F_m(x)=F(x)^m=\sum_{n=0}^{\infty}t_m(n) x^n,$$ and proposed a conjecture stating that for any given integer $m\ge 2$, the sequence $\{t_m(n)\}_{n=0}^{\infty}$ is unbounded. In this paper, we introduce a new method to investigate this conjecture. In fact, by making use of algebraic, $p$-adic and analytic methods, we show that the Gawron-Miska-Ulas conjecture is true.

Submission history

From: Shaofang Hong [view email]
[v1] Wed, 24 Jun 2026 13:44:27 UTC (15 KB)