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Structure-aware optimal intervention for rumor dynamics on networks: Node-level, time-varying, and resource-constrained 网络谣言动态的结构感知最优干预：节点级、时变和资源约束

IF 7.8 1区数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Chaos Solitons & Fractals

Pub Date : 2026-02-09 DOI: 10.1016/j.chaos.2026.118036

Yan Zhu, Qingyang Liu, Chang Guo, Tianlong Fan, Linyuan Lü

Rumor propagation in social networks undermines social stability and public trust, calling for interventions that are both effective and resource-efficient. We develop a node-level, time-varying optimal intervention framework that allocates limited resources according to the evolving diffusion state. Unlike static, centrality-based heuristics, our approach derives control weights by solving a resource-constrained optimal control problem tightly coupled to the network structure. Across synthetic and real-world networks, the method consistently lowers both the infection peak and the cumulative infection area relative to uniform and centrality-based static allocations. Moreover, it reveals a stage-aware law: early resources prioritize influential hubs to curb rapid spread, whereas later resources shift to peripheral nodes to eliminate residual transmission. By integrating global efficiency with fine-grained adaptability, the framework offers a scalable and interpretable paradigm for misinformation management and crisis response.

社交网络中的谣言传播破坏了社会稳定和公众信任，需要有效和资源高效的干预。我们开发了一个节点级、时变的最优干预框架，根据不断变化的扩散状态分配有限的资源。与静态的、基于中心性的启发式方法不同，我们的方法通过解决与网络结构紧密耦合的资源约束最优控制问题来获得控制权重。在合成网络和实际网络中，相对于统一的和基于中心性的静态分配，该方法始终能够降低感染峰值和累积感染面积。此外，它揭示了一个阶段感知规律：早期资源优先考虑有影响力的中心以遏制快速传播，而后期资源转移到外围节点以消除残余传播。通过将全局效率与细粒度适应性相结合，该框架为错误信息管理和危机响应提供了可扩展且可解释的范例。

引用次数: 0

Book Review:; An Introduction to Stellarators 书评:;仿星器简介

IF 10.2 1区数学 Q1 MATHEMATICS, APPLIED

SIAM Review

Pub Date : 2026-02-09 DOI: 10.1137/25m1728582

Georg Stadler

SIAM Review, Volume 68, Issue 1, Page 213-214, February 2026.
Stellarators are devices used in plasma physics to confine very hot plasmas (i.e., ionized gases) with magnetic fields to sustain nuclear fusion reactions. Fusion is the Sun’s energy source, and the achievement of sustained fusion on Earth has been studied for several decades as a promising source of clean and safe energy. Unlike tokamaks, which use a combination of simple magnetic fields and plasma current to cage the plasma, stellarators rely solely on external magnetic fields. This has potential advantages for sustained fusion energy production, but requires the design of complicated magnetic fields and expensive-to-build, complex electromagnetic coils.

《SIAM评论》，第68卷，第1期，213-214页，2026年2月。仿星器是等离子体物理学中用于用磁场限制非常热的等离子体（即电离气体）以维持核聚变反应的装置。核聚变是太阳的能量来源，在地球上实现持续核聚变作为一种清洁和安全的有前途的能源已经研究了几十年。与托卡马克利用简单的磁场和等离子体电流的组合来束缚等离子体不同，仿星器完全依靠外部磁场。这对于持续的核聚变能源生产具有潜在的优势，但需要设计复杂的磁场和昂贵的制造，复杂的电磁线圈。

引用次数: 0

Quantitative Estimates: How Well Does the Discrete Fourier Transform Approximate the Fourier Transform on [math] 定量估计：离散傅立叶变换在数学上近似傅立叶变换有多好

IF 10.2 1区数学 Q1 MATHEMATICS, APPLIED

SIAM Review

Pub Date : 2026-02-09 DOI: 10.1137/24m1650399

Martin Ehler, Karlheinz Gröchenig, Andreas Klotz

SIAM Review, Volume 68, Issue 1, Page 93-123, February 2026.
Abstract. In order to compute the Fourier transform of a function [math] on the real line numerically, one samples [math] on a grid and then takes the discrete Fourier transform. We derive exact error estimates for this procedure in terms of the decay and smoothness of [math]. The analysis provides an asymptotically optimal recipe for how to relate the number of samples, the sampling interval, and the grid size.

SIAM评论，第68卷，第1期，第93-123页，2026年2月。摘要。为了在实数线上数值计算函数的傅里叶变换，在网格上采样，然后进行离散傅里叶变换。我们根据[数学]的衰减和平滑度推导出这个过程的精确误差估计。该分析为如何将样本数量、采样间隔和网格大小联系起来提供了一个渐近最优配方。

引用次数: 0

A fault-tolerant dynamic graph attention network for energy efficient routing and reliability in wireless sensor networks 面向无线传感器网络节能路由和可靠性的容错动态图关注网络

IF 1 4区数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Combinatorial Optimization

Pub Date : 2026-02-09 DOI: 10.1007/s10878-026-01396-6

G. V. Soni Meera, R. Isaac Sajan

引用次数: 0

Featured Review:; Introduction to Probability and Its Applications 评论:;概率论及其应用

IF 10.2 1区数学 Q1 MATHEMATICS, APPLIED

SIAM Review

Pub Date : 2026-02-09 DOI: 10.1137/24m1717579

James N. MacLaurin

SIAM Review, Volume 68, Issue 1, Page 207-208, February 2026.
The two volumes of Feller’s Introduction to Probability are classic and comprehensive. They span a huge range of topics, starting from measure theory and basic probability distributions and moving on to topics such as Markov chains and the central limit theorem. The books are very well written and accessible. It seems that most of the theory that is being employed in 2025 by applied modelers and analysts is touched on in these books in some way. Of course, the emphases are different. Much of the foundational material on probability theory has not changed a lot since Feller’s two volumes, including basic measure theory, the law of large numbers and the central limit theorem, conditional probabilities, and theorems such as the Borel Cantelli Lemma. In contrast, it seems that Feller spends a lot of time surveying a range of special probability models and distributions whereas more modern treatments would perhaps spend more time in surveying general analytic techniques. This is probably partly due to the fact that numerical simulation techniques were not nearly as powerful when the book was written, so scholars tended to focus more on specific tractable models.

SIAM评论，68卷，第1期，207-208页，2026年2月。Feller的两卷本概率论是经典而全面的。它们涵盖了广泛的主题，从度量理论和基本概率分布开始，到马尔可夫链和中心极限定理等主题。这些书写得很好，通俗易懂。在2025年，应用建模者和分析师所使用的大部分理论似乎都以某种方式在这些书中有所涉及。当然，重点是不同的。自Feller的两卷书以来，概率论的许多基础材料并没有发生太大变化，包括基本测度理论、大数定律和中心极限定理、条件概率以及Borel Cantelli引理等定理。相比之下，Feller似乎花了很多时间研究一系列特殊的概率模型和分布，而更现代的处理方法可能会花更多的时间研究一般的分析技术。这在一定程度上可能是由于在撰写本书时，数值模拟技术还没有那么强大，因此学者们倾向于更多地关注具体的可处理模型。

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引用次数: 0

Education 教育

IF 10.2 1区数学 Q1 MATHEMATICS, APPLIED

SIAM Review

Pub Date : 2026-02-09 DOI: 10.1137/25m1799234

Hélène Frankowska

SIAM Review, Volume 68, Issue 1, Page 151-151, February 2026.

SIAM评论，第68卷，第1期，第151-151页，2026年2月。

引用次数: 0

Towards Interpretable Deep Generative Models via Causal Representation Learning 通过因果表示学习实现可解释的深度生成模型

IF 3.7 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association

Pub Date : 2026-02-09 DOI: 10.1080/01621459.2026.2620154

Gemma Moran, Bryon Aragam

引用次数: 0

Parameter Identifiability, Parameter Estimation, and Model Prediction for Differential Equation Models 微分方程模型的参数可辨识性、参数估计和模型预测

IF 10.2 1区数学 Q1 MATHEMATICS, APPLIED

SIAM Review

Pub Date : 2026-02-09 DOI: 10.1137/24m1667968

Matthew J. Simpson, Ruth E. Baker

SIAM Review, Volume 68, Issue 1, Page 153-171, February 2026.
Abstract. Interpreting data with mathematical models is an important aspect of real-world industrial and applied mathematical modeling. Often we are interested to understand the extent to which a particular set of data informs and constrains model parameters. This question is closely related to the concept of parameter identifiability, and in this article we present a series of computational exercises to introduce tools that can be used to assess parameter identifiability, estimate parameters, and generate model predictions. Taking a likelihood-based approach, we show that very similar ideas and algorithms can be used to deal with a range of different mathematical modeling frameworks. The exercises and results presented in this article are supported by a suite of open access codes that can be accessed on GitHub.

SIAM评论，第68卷，第1期，第153-171页，2026年2月。摘要。用数学模型解释数据是现实世界工业和应用数学建模的一个重要方面。通常，我们感兴趣的是了解特定数据集通知和约束模型参数的程度。这个问题与参数可识别性的概念密切相关，在本文中，我们提出了一系列计算练习，以介绍可用于评估参数可识别性、估计参数和生成模型预测的工具。采用基于可能性的方法，我们展示了非常相似的思想和算法可以用于处理一系列不同的数学建模框架。本文中提供的练习和结果由一套开放访问代码支持，这些代码可以在GitHub上访问。

引用次数: 0

Deep learning for the change-point Cox model with current status data. 基于当前状态数据的深度学习变点Cox模型。

IF 1 3区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Lifetime Data Analysis

Pub Date : 2026-02-09 DOI: 10.1007/s10985-026-09689-y

Qiyue Huang, Anyin Feng, Qiang Wu, Xingwei Tong

This study develops estimation methods for a deep partially linear Cox proportional hazards model with a change point under current status data, aiming to accommodate complex change-point effects. Prior work has largely relied on linear models, which may inadequately capture relationships among multivariate covariates and thus hinder accurate change-point detection. To address this, we use a deep neural network to model covariate effects within the Cox framework and propose a maximum likelihood estimation procedure for the model. We establish asymptotic properties of the resulting estimators, including consistency, asymptotic independence, and semiparametric efficiency. Simulation studies indicate that the proposed inference procedure performs well in finite samples. An analysis of a breast cancer dataset is provided to illustrate the methodology.

为了适应复杂的变化点效应，研究了在当前状态数据下带变化点的深度部分线性Cox比例风险模型的估计方法。先前的工作很大程度上依赖于线性模型，这可能无法充分捕捉多变量协变量之间的关系，从而妨碍准确的变化点检测。为了解决这个问题，我们使用深度神经网络对Cox框架内的协变量效应进行建模，并提出了模型的最大似然估计过程。我们建立了所得到的估计量的渐近性质，包括相合性、渐近独立性和半参数有效性。仿真研究表明，所提出的推理方法在有限样本下具有良好的性能。本文提供了对乳腺癌数据集的分析来说明该方法。

引用次数: 0

Large domain homomorphic evaluation for BFV-like schemes via ring repacking 基于环重包装的类bfv方案的大域同态评估

IF 1.6 2区数学 Q3 COMPUTER SCIENCE, THEORY & METHODS

Designs, Codes and Cryptography

Pub Date : 2026-02-09 DOI: 10.1007/s10623-025-01782-x

Jean-Philippe Bossuat, Malika Izabachene

引用次数: 0

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