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Retinal artery/vein (A/V) classification plays a critical role in the clinical biomarker study of how various systemic and cardiovascular diseases affect the retinal vessels. Conventional methods of automated A/V classification are generally complicated and heavily depend on the accurate vessel segmentation. In this paper, we propose a multi-task deep neural network with spatial activation mechanism that is able to segment full retinal vessel, artery and vein simultaneously, without the pre-requirement of vessel segmentation. The input module of the network integrates the domain knowledge of widely used retinal preprocessing and vessel enhancement techniques. We specially customize the output block of the network with a spatial activation mechanism, which takes advantage of a relatively easier task of vessel segmentation and exploits it to boost the performance of A/V classification. In addition, deep supervision is introduced to the network to assist the low level layers to extract more semantic information. The proposed network achieves pixel-wise accuracy of 95.70% for vessel segmentation, and A/V classification accuracy of 94.50%, which is the state-of-the-art performance for both tasks on the AV-DRIVE dataset. Furthermore, we have also tested the model performance on INSPIRE-AVR dataset, which achieves a skeletal A/V classification accuracy of 91.6%.

With the growing popularity of the fifth-generation (5G) wireless systems and cloud-enabled Internet of Vehicles, vehicular cloud has been introduced as a novel mobile device computing mode, which enables vehicles to offload their computation-intensive tasks to neighbors. In this paper, we first present a 5G cloud-enabled scenario of vehicular cloud computing where a vehicular terminal works either as a service provider with idle computation resources or a requestor who has a computation-intensive task that can be executed either locally or offloaded to nearby providers via opportunistic vehicle-to-vehicle communications. Then, we study the following issues: 1) how to determine the appropriate offloading rate of requestors; 2) how to select the most appropriate computation service provider; 3) how to identify the ideal pricing strategy for each service provider. We address the above-mentioned problems by developing a two-player Stackelberg-game-based opportunistic computation offloading scheme under situations involving complete and incomplete information that primarily considers task completion duration and service price. We simplify the former case into a common resource assignment problem with mathematical solutions. For the latter case, Stackelberg equilibriums of the offloading game are derived, and the corresponding existence conditions are concretely discussed. Finally, a Monte-Carlo simulation-based performance evaluation shows that the proposed methods can significantly reduce the task completion duration while ensuring the profit of service providers, thus achieving mutually satisfactory computation offloading decisions. INDEX TERMS Computation offloading, 5G cloud-enabled IoV, vehicle-to-vehicle communication, Stackelberg equilibrium.

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The Hu-12 Block, located in ZhongYuan Oilfield, Henan Province China, contains many small but highly heterogeneous oil reservoirs, with low permeability oil bearing formations and high permeability mixed (oil/water) layers. The reservoir temperature is 90 o C, and the original reservoir pressure of nearly 25 MPa, and with high salinity of formation water (around 200,000 mg/l). After 20 years of water injection, the recovery factor achieved was only 20-25%, and average water cut has reached to over 95%. N 2 gas injection has been tried with less success due to early gas breakthrough from high permeability zones. Since 2006, high pressure air foam and air injection (Air Foam Alternative Air Injection, AFAAI) has been proposed and implemented in one of the reservoirs, in order to block high permeability water zones and increase the sweeping efficiency of air and water injection. A series of laboratory experiments have been conducted to study the oxidation kinetics of air/air foam with oil and the blocking and displacement efficiency of air foams in different oil sands. Reservoir simulation has also been carried out for predicting the reservoir response to air foam injection and optimizing the injection process. Air foam and air injection was started in the field since May 2007 in a well group with 1 injector and 4 producers, using a small high pressure air compressor (40 MPa, 7 m 3 /min air rate). Up to now, 460,000 Nm 3 air and 2920 m 3 foam surfactant solution have been injected into the reservoir. The field results show that no oxygen/N 2 breakthrough was observed and a significant increase in oil production with water cut reduced by 4%. The detailed laboratory study and field experience are presented in this paper.

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