In this paper we investigate spatiotemporal pattern formation in excitable media with only a long-range link. Besides the trivial solutions of spiral patterns, we find the asymptotic self-sustained target waves in the...In this paper we investigate spatiotemporal pattern formation in excitable media with only a long-range link. Besides the trivial solutions of spiral patterns, we find the asymptotic self-sustained target waves in the autonomous tissues. The wave source supporting this kind of new pattern is the oscillatory one-dimensional Winfree-loop self- organized under the presence of a long-range link, which is explored by the dominant phase-advanced driving method. Based on this understanding we can effectively regulate the oscillations of excitable media by suitably arranging the long-range link, including construction of self-sustained target waves with controllable period and wave length, or manipulation of system states between different patterns.展开更多
The influence of long-range links on spiral waves in an excitable medium has been investigated. Spatiotemporal dynamics in an excitable small-world network transform remarkably when we increase the long-range connecti...The influence of long-range links on spiral waves in an excitable medium has been investigated. Spatiotemporal dynamics in an excitable small-world network transform remarkably when we increase the long-range connection probability P. Spiral waves with few perturbations, broken spiral waves, pseudo spiral turbulence, synchronous oscillations, and homogeneous rest state are discovered under different network structures. Tip number is selected to detect non-equilibrium phase transition between different spatiotemporal patterns. The Kuramoto order parameter is used to identify these patterns and explain the emergence of the rest state. Finally, we use long-range links to successfully control spiral waves and spiral turbulence.展开更多
动态网络链路预测广泛的应用前景,使得其逐渐成为网络科学研究的热点.动态网络链路演化过程中具有复杂的空间相关性和时间依赖性,导致其链路预测任务极具挑战.提出一个基于时序图卷积的动态网络链路预测模型(dynamic network link predi...动态网络链路预测广泛的应用前景,使得其逐渐成为网络科学研究的热点.动态网络链路演化过程中具有复杂的空间相关性和时间依赖性,导致其链路预测任务极具挑战.提出一个基于时序图卷积的动态网络链路预测模型(dynamic network link prediction based on sequential graph convolution, DNLP-SGC).针对网络快照序列不能有效反映动态网络连续性的问题,采用边缘触发机制对原始网络权重矩阵进行修正,弥补了离散快照表示动态网络存在时序信息丢失的不足.从网络演化过程出发,综合考虑节点间的特征相似性以及历史交互信息,采用时序图卷积提取动态网络中节点的特征,该方法融合了节点时空依赖关系.进一步,采用因果卷积网络捕获网络演化过程中潜在的全局时序特征,实现动态网络链路预测.在2个真实的网络数据集上的实验结果表明,DNLP-SGC在precision, recall, AUC指标上均优于对比的基线模型.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 11047146)the Science Foundation of the Education Bureau of Shaanxi Province of China (Grant No. 11JK0544)+1 种基金the Natural Science Foundation of Shaanxi Province of China (Grant No. 2010JQ1014)the Science Foundation of Baoji University of Arts and Sciences (Grant Nos. ZK1048 andZK1049)
文摘In this paper we investigate spatiotemporal pattern formation in excitable media with only a long-range link. Besides the trivial solutions of spiral patterns, we find the asymptotic self-sustained target waves in the autonomous tissues. The wave source supporting this kind of new pattern is the oscillatory one-dimensional Winfree-loop self- organized under the presence of a long-range link, which is explored by the dominant phase-advanced driving method. Based on this understanding we can effectively regulate the oscillations of excitable media by suitably arranging the long-range link, including construction of self-sustained target waves with controllable period and wave length, or manipulation of system states between different patterns.
基金Project supported by the National Natural Science Foundation of China (Grant No. 11105003)the Science Foundation of the Education Bureau of Shaanxi Province of China (Grant No. 11JK0544)
文摘The influence of long-range links on spiral waves in an excitable medium has been investigated. Spatiotemporal dynamics in an excitable small-world network transform remarkably when we increase the long-range connection probability P. Spiral waves with few perturbations, broken spiral waves, pseudo spiral turbulence, synchronous oscillations, and homogeneous rest state are discovered under different network structures. Tip number is selected to detect non-equilibrium phase transition between different spatiotemporal patterns. The Kuramoto order parameter is used to identify these patterns and explain the emergence of the rest state. Finally, we use long-range links to successfully control spiral waves and spiral turbulence.
文摘动态网络链路预测广泛的应用前景,使得其逐渐成为网络科学研究的热点.动态网络链路演化过程中具有复杂的空间相关性和时间依赖性,导致其链路预测任务极具挑战.提出一个基于时序图卷积的动态网络链路预测模型(dynamic network link prediction based on sequential graph convolution, DNLP-SGC).针对网络快照序列不能有效反映动态网络连续性的问题,采用边缘触发机制对原始网络权重矩阵进行修正,弥补了离散快照表示动态网络存在时序信息丢失的不足.从网络演化过程出发,综合考虑节点间的特征相似性以及历史交互信息,采用时序图卷积提取动态网络中节点的特征,该方法融合了节点时空依赖关系.进一步,采用因果卷积网络捕获网络演化过程中潜在的全局时序特征,实现动态网络链路预测.在2个真实的网络数据集上的实验结果表明,DNLP-SGC在precision, recall, AUC指标上均优于对比的基线模型.
