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AbstractWe study the decisions of a supplier and retailer in a cooperative advertising program to sell divisible products to consumers in a social network with positive externalities. The supplier decides its participation rate, the retailer with a limited budget decides discriminative advertising expenditure for consumers, and consumers decide their purchase levels. In this study, we solve the subgame perfect equilibrium of the game between the supplier, retailer, and consumers. The equilibrium leads to a new demand function of advertising expenditure, which is different from those demand functions typically assumed in existing studies. In the equilibrium, the higher a consumer's network influence quantified by Bonacich centrality, the higher is the retailer's advertising expenditure on the consumer. We characterize the supplier's participation rate by two network‐dependent thresholds. If the retailer's budget level is lower than one threshold or higher than the other, the participation rate will not depend on the network structure. Although both parties' profits and the retailer's advertising expenditure increase with the network density, the supplier's participation rate decreases with it. Finally, the retailer's profits can be improved significantly by exploiting the network information and advertising cooperatively.

The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above. ; Terahertz imaging (frequency between 0.1 to 10 THz) is a modern technique for public security check. Due to poor imaging quality, traditional machine vision methods often fail to detect concealed weapons in Terahertz samples, while modern instance segmentation approaches have complex multiple-stage concatenation and often hunger for massive and accurate training data. In this work, we realize a novel Conditional Generative Adversarial Nets (CGANs), named as Mask-CGANs to segment weapons in such a challenging imaging quality. The Mask-Generator network employs a "selected-connection U-Net" to restrain false alarms and speed up training convergence. The loss function takes reconstruction errors and sparse priors into consideration to preserve precise segmentation. Such a learning architecture works well with a small training dataset. Experiments show that the proposed model outperforms CGANs (more than 16–32% in Recall, Precision and Accuracy) and Mask-RCNN (more than 3–6%). Moreover, its testing speed (69.7 FPS) is fast enough to be implemented in a real-time security check system, which is 44 times faster than Mask-RCNN. In the experiments for mammographic mass segmentation on INBreast dataset, the Dice index of the proposed method is 91.29, surpasses the-state-of-the-art medical issue segmentation methods. The full implementation (based on TensorFlow) is available at https://github.com/JXPanzz/THz). ; This work is supported by the National Key R&D Program of China under Grant 2017YFB0802300, National Natural Science Foundation of China61601223, Natural Science Foundation of Jiangsu ProvinceBK20150756, Postdoctoral Science Foundation of China (Top level)2015M580427. H. Zhou was supported by UK EPSRC under Grant EP/N011074/1, Royal Society-Newton Advanced Fellowship under Grant NA160342, and European Union's Horizon 2020 research and innovation program under the Marie-Sklodowska-Curie grant agreement No 720325. ; Peer-reviewed ; Post-print

Eco-industrial development is widely considered as an effective policy and a business concept to realize sustainable circularization through collaborative networks among industries. Japanese government has accumulated practices as its national Eco Town Program in 26 cities since 1997. The operation of facilities and policy implementation have provided lessons and suggestions particularly for industrializing cities who desperately seek for the sustainable solutions achieving environment management and economic growth simultaneously. This paper aims to review the policy framework as well as accomplishments of the Eco Town Program and to provide lessons and suggestions for industrial cities' management. We reviewed and analyzed the experience of the Eco Town Program for a decade from viewpoints of the policy framework and circular situation; and provided general implications such as combination of recycle technologies and social system, symbiotic network among recycle entities and energy intensive industries as well as suitable locational planning.