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Focusing on the tendency of terrorist organizations to explosive attack, this article applied the institutional theory as the basis to explain the inherent logic of attack type similarity from the perspective of mimetic, coercive, and normative isomorphism. Subsequently, the study conducted an empirical analysis of the data onto 1825 terrorist organizations recorded in the Global Terrorism Database with the logistic regression method. The results show that: (1) Terrorist organizations will learn from pre-existing terrorist organizations' experiences, and mimetic isomorphism will promote explosive tendency; (2) Due to the normative isomorphism effect, terrorist groups' tendency to explosive attacks is weakened by their increased duration; (3) If terrorist organizations are hostile to a strong government, coercive isomorphism positively moderates the negative effects of increasing duration. The study suggests that counter-terrorism approaches such as destroying the learnable experience of attacks, addressing the root causes of terrorism, and maintaining a strong government may be helpful in stopping increasing terrorist activities, which is essential for reducing terrorist organizations' vivosphere, blocking the inter-flow and imitation between terrorist organizations, and ultimately interrupting the terrorist propagation chain.

Proceeding of: 2020 IEEE International Symposium on Information Theory (ISIT), Los Angeles, CA, USA, 21-26 June 2020 ; This paper concerns the maximum coding rate at which a code of given blocklength can be transmitted with a given block-error probability over a non-coherent Rayleigh block-fading channel with multiple transmit and receive antennas (MIMO). In particular, a high-SNR normal approximation of the maximum coding rate is presented, which is proved to become accurate as the signal-to-noise ratio (SNR) and the number of coherence intervals L tend to infinity. ; C. Qi and T. Koch have received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant No. 714161). T. Koch has further received funding from the Spanish Ministerio de Economía y Competitividad under Grants RYC-2014-16332 and TEC2016-78434-C3-3-R (AEI/FEDER, EU)

AbstractPredicting terrorism risk is crucial for formulating detailed counter‐strategies. However, this task is challenging mainly because the risk of the concerned potential victim is not isolated. Terrorism risk has a spatiotemporal interprovincial contagious characteristic. The risk diffusion mechanism comes from three possibilities: cross‐provincial terrorist attacks, internal and external echoes, and internal self‐excitation. This study proposed a novel spatiotemporal graph convolutional network (STGCN)‐based extension method to capture the complex and multidimensional non‐Euclidean relationships between different provinces and forecast the daily risks. Specifically, three graph structures were constructed to represent the contagious process between provinces: the distance graph, the province‐level root cause similarity graph, and the self‐excited graph. The long short‐term memory and self‐attention layers were extended to STGCN for capturing context‐dependent temporal characters. At the same time, the one‐dimensional convolutional neural network kernel with the gated linear unit inside the classical STGCN can model single‐node‐dependent temporal features, and the spectral graph convolution modules can capture spatial features. The experimental results on Afghanistan terrorist attack data from 2005 to 2020 demonstrate the effectiveness of the proposed extended STGCN method compared to other machine learning prediction models. Furthermore, the results illustrate the crucial of capturing comprehensive spatiotemporal correlation characters among provinces. Based on this, this article provides counter‐terrorism management insights on addressing the long‐term root causes of terrorism risk and performing short‐term situational prevention.