In this paper, we revisit two popular convolu-tional neural networks (CNN) in person re-identification (re-ID), i.e.,verification and identification models. The two models have their respective advantages and limitations due to different loss functions. In this paper, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a siamese network that simultaneously computes the identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two input images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus making full usage of the re-ID annotations. Our method can be easily applied on different pre-trained networks. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show our architecture can also be applied in image retrieval.

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With the blasting increase of wireless data traffic, incumbent wireless service providers (WSPs) face critical challenges in provisioning spectrum resource. Given the permission of unlicensed access to TV white spaces, WSPs can alleviate their burden by exploiting the concept of "capacity offload" to transfer part of their traffic load to unlicensed spectrum. For such use cases, a central problem is for WSPs to coexist with others, since all of them may access the unlicensed spectrum without coordination thus interfering each other. Game theory provides tools for predicting the behavior of WSPs, and we formulate the coexistence problem under the framework of non-cooperative games as a capacity offload game (COG). We show that a COG always possesses at least one pure-strategy Nash equilibrium (NE), and does not have any mixed-strategy NE. The analysis provides a full characterization of the structure of the NEs in two-player COGs. When the game is played repeatedly and each WSP individually updates its strategy based on its best-response function, the resulting process forms a best-response dynamic. We establish that, for two-player COGs, alternating-move best-response dynamics always converge to an NE, while simultaneous-move best-response dynamics does not always converge to an NE when multiple NEs exist. When there are more than two players in a COG, if the network configuration satisfies certain conditions so that the resulting best-response dynamics become linear, both simultaneous-move and alternating-move best-response dynamics are guaranteed to converge to the unique NE. Index Termsbest response; capacity offload; Nash equilibrium; non-cooperative game; power allocation; unlicensed spectrum FZ and WZ are with