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

Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
G
Google Developers Blog
S
SegmentFault 最新的问题
Microsoft Security Blog
Microsoft Security Blog
J
Java Code Geeks
罗磊的独立博客
H
Hackread – Cybersecurity News, Data Breaches, AI and More
量子位
P
Proofpoint News Feed
博客园 - 【当耐特】
MongoDB | Blog
MongoDB | Blog
L
LangChain Blog
F
Fortinet All Blogs
C
Check Point Blog
博客园_首页
I
InfoQ
Jina AI
Jina AI
Blog — PlanetScale
Blog — PlanetScale
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
酷 壳 – CoolShell
酷 壳 – CoolShell
Engineering at Meta
Engineering at Meta
美团技术团队
Vercel News
Vercel News
Apple Machine Learning Research
Apple Machine Learning Research

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
New results on proper orientation number of graphs
Laochao Wang Xiaolin Wang, Guangmiao Yu · 2026-04-16 · via math updates on arXiv.org

The proper orientation number $\vecχ(G)$ of an undirected graph $G$ is the minimum $k$ such that there exists an orientation of $G$ with all out-degrees at most $k$ and with different out-degrees for any two adjacent vertices. Chen, Mohar and Wu (JCTB, 2023) proved that if $G$ is a $r$-partite graph, then $\vecχ(G) \leq \frac{1}{2} \text{Mad}(G)+r^{1+o(1)}$, where $\text{Mad}(G)$ is the maximum average degree of $G$. Moreover, if $G$ is a bipartite graph, then $ \vecχ(G) \leq \lceil \frac{1}{2} \text{Mad}(G)\rceil +3$ and this bound is tight. They also asked whether $\vecχ(G)-\lceil \frac{1}{2} \text{Mad}(G)\rceil$ can be bounded by a linear function of $r$. In this paper, we first construct somewhat involved $r$-partite graphs with $\vecχ(G)\geq\lceil \frac{1}{2} \text{Mad}(G)\rceil +\lfloor\frac{5}{2}r\rfloor-2$, showing that a linear dependence on \(r\) is unavoidable. We also prove that $ \vecχ(G) \leq\lceil \frac{1}{2} \text{Mad}(G)\rceil +7$ for every 3-partite graph $G$. This implies \(\vecχ(G)\le 10\) for \(3\)-colorable planar graphs and \(\vecχ(G)\le 9\) for outerplanar graphs, improving the corresponding bounds of Chen, Mohar, and Wu.