Strategic games on a hierarchical network model

Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.

Probability-Based Channel Pruning for Depthwise Separable Convolutional Networks

Channel pruning can reduce memory consumption and running time with least performance damage,and is one of the most important techniques in network compression.However,existing channel pruning methods mainly focus on the pruning of standard convolutional networks,and they rely intensively on time-consuming fine-tuning to achieve the performance improvement.To this end,we present a novel efficient probability-based channel pruning method for depthwise separable convolutional networks.Our method leverages a new simple yet effective probability-based channel pruning criterion by taking the scaling and shifting factors of batch normalization layers into consideration.A novel shifting factor fusion technique is further developed to improve the performance of the pruned networks without requiring extra time-consuming fine-tuning.We apply the proposed method to five representative deep learning networks,namely MobileNetV1,MobileNetV2,ShuffleNetV1,ShuffleNetV2,and GhostNet,to demonstrate the efficiency of our pruning method.Extensive experimental results and comparisons on publicly available CIFAR10,CIFAR100,and ImageNet datasets validate the feasibility of the proposed method.

Two-level hierarchical feature learning for image classification

In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.

Modeling dual-scale epidemic dynamics on complex networks with reaction diffusion processes

The frequent outbreak of severe foodborne diseases(e.g., haemolytic uraemic syndrome and Listeriosis) in 2011 warns of a potential threat that world trade could spread fatal pathogens(e.g., enterohemorrhagic Escherichia coli). The epidemic potential from trade involves both intra-proliferation and inter-diffusion. Here, we present a worldwide vegetable trade network and a stochastic computational model to simulate global trade-mediated epidemics by considering the weighted nodes and edges of the network and the dual-scale dynamics of epidemics. We address two basic issues of network structural impact in global epidemic patterns:(1) in contrast to the prediction of heterogeneous network models, the broad variability of node degree and edge weights of the vegetable trade network do not determine the threshold of global epidemics;(2) a ‘penetration effect', by which community structures do not restrict propagation at the global scale, quickly facilitates bridging the edges between communities, and leads to synchronized diffusion throughout the entire network. We have also defined an appropriate metric that combines dual-scale behavior and enables quantification of the critical role of bridging edges in disease diffusion from widespread trading. The unusual structure mechanisms of the trade network model may be useful in producing strategies for adaptive immunity and reducing international trade frictions.