Locally interacting dynamical systems, such as epidemic spread, rumor
propagation through crowd, and forest fire, exhibit complex global dynamics
originated from local, relatively simple, and often stochastic interactions
between dynamic elements. Their temporal evolution is often driven by
transitions between a finite number of discrete states. Despite significant
advancements in predictive modeling through deep learning, such interactions
among many elements have rarely explored as a specific domain for predictive
modeling. We present Attentive Recurrent Neural Cellular Automata (AR-NCA), to
effectively discover unknown local state transition rules by associating the
temporal information between neighboring cells in a permutation-invariant
manner. AR-NCA exhibits the superior generalizability across various system
configurations (i.e., spatial distribution of states), data efficiency and
robustness in extremely data-limited scenarios even in the presence of
stochastic interactions, and scalability through spatial dimension-independent
prediction.

局部相互作用动力系统，如流行病传播、谣言传播和森林火灾，表现出由动态元素之间的局部、相对简单和通常随机的相互作用产生的复杂全局动态。我们提出了 AR-NCA（Attention Recurrent Neural Cellular Automata），通过以置换 - 不变的方式在相邻细胞之间关联时间信息，从而有效发现未知的局部状态转换规则。AR-NCA 在各种系统配置（即状态的空间分布）、极度数据有限的情况下，甚至在存在随机相互作用的情况下，显示出卓越的泛化能力、数据效率和鲁棒性，以及空间维度无关的可扩展性。

学习局部交互离散动力系统：迈向高效数据和可扩展预测

Learning Locally Interacting Discrete Dynamical Systems: Towards  Data-Efficient and Scalable Prediction

A key challenge in Bayesian decentralized data fusion is the `rumor
propagation' or `double counting' phenomenon, where previously sent data
circulates back to its sender. It is often addressed by approximate methods
like covariance intersection (CI) which takes a weighted average of the
estimates to compute the bound. The problem is that this bound is not tight,
i.e. the estimate is often over-conservative. In this paper, we show that by
exploiting the probabilistic independence structure in multi-agent
decentralized fusion problems a tighter bound can be found using (i) an
expansion to the CI algorithm that uses multiple (non-monolithic) weighting
factors instead of one (monolithic) factor in the original CI and (ii) a
general optimization scheme that is able to compute optimal bounds and fully
exploit an arbitrary dependency structure. We compare our methods and show that
on a simple problem, they converge to the same solution. We then test our new
non-monolithic CI algorithm on a large-scale target tracking simulation and
show that it achieves a tighter bound and a more accurate estimate compared to
the original monolithic CI.

通过利用多智能体分散融合问题中的概率独立结构，本文展示了一种使用多个 (非整体的) 权重因子而不是原始 CI 中的单个 (整体的) 因子的 CI 算法扩展以及一种能够计算最优上下界并充分利用任意依赖结构的通用优化方案，从而可以找到一个更紧密的界限。通过比较我们的方法，并展示在一个简单问题上收敛于相同的解。然后，我们在一个大规模目标跟踪模拟中测试我们的新非整体的 CI 算法，展示相比原始整体 CI，它实现了更紧密的界限和更准确的估计。

基于结构的最优多智能体贝叶斯分散估计

Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized  Estimation

The identification of the most influential spreaders in networks is important
to control and understand the spreading capabilities of the system as well as
to ensure an efficient information diffusion such as in rumor-like dynamics.
Recent works have suggested that the identification of influential spreaders is
not independent of the dynamics being studied. For instance, the key disease
spreaders might not necessarily be so when it comes to analyze social contagion
or rumor propagation. Additionally, it has been shown that different metrics
(degree, coreness, etc) might identify different influential nodes even for the
same dynamical processes with diverse degree of accuracy. In this paper, we
investigate how nine centrality measures correlate with the disease and rumor
spreading capabilities of the nodes that made up different synthetic and
real-world (both spatial and non-spatial) networks. We also propose a
generalization of the random walk accessibility as a new centrality measure and
derive analytical expressions for the latter measure for simple network
configurations. Our results show that for non-spatial networks, the $k$-core
and degree centralities are most correlated to epidemic spreading, whereas the
average neighborhood degree, the closeness centrality and accessibility are
most related to rumor dynamics. On the contrary, for spatial networks, the
accessibility measure outperforms the rest of centrality metrics in almost all
cases regardless of the kind of dynamics considered. Therefore, an important
consequence of our analysis is that previous studies performed in synthetic
random networks cannot be generalized to the case of spatial networks.

该论文研究了九种中心度量如何与不同合成和现实网络中的疾病和传言传播的节点的扩散能力相关，并提出了一种随机游走可达性的新中心度量。研究结果表明，在非空间网络中，k - 核心和度中心度量与流行病传播最相关，而平均邻域度，紧密中心度和可达性则与谣言传播最相关，而在空间网络中，可达性度量在几乎所有情况下优于其余中心度量。