Image Labeling on a Network: Using Social-Network Metadata for Image Classification

Large-scale image retrieval benchmarks invariably consist of images from the Web. Many of these benchmarks are derived from online photo sharing networks, like Flickr, which in addition to hosting images also provide a highly interactive social community. Such communities generate rich metadata that can naturally be harnessed for image classification and retrieval. Here we study four popular benchmark datasets, extending them with social-network metadata, such as the groups to which each image belongs, the comment thread associated with the image, who uploaded it, their location, and their network of friends. Since these types of data are inherently relational, we propose a model that explicitly accounts for the interdependencies between images sharing common properties. We model the task as a binary labeling problem on a network, and use structured learning techniques to learn model parameters. We find that social-network metadata are useful in a variety of classification tasks, in many cases outperforming methods based on image content.Comment: ECCV 2012; 14 pages, 4 figure

Optimization of cathode flooding in scaled-up microfluidic fuel cells

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Numerical simulation on the local impact of an operating wind trubine

Session VII - Energy Optimization II: no. 5Wind energy is commonly considered to be a clean and environmentally friendly renewable energy resource, as they do not pollute our atmosphere with greenhouse gas, nor do they cause any radioactive problems compared to nuclear energy. However, there are still some environmental impacts due to the installation and operation of the wind turbines that cannot be ignored, such as noise, visual and climatic impact. Especially, the observed local climate change in some wind turbine areas has attracted general concern in recent years. Experts suspected that the a long time operation of ...postprin

HEA-Loc: A robust localization algorithm for sensor networks of diversified topologies

In recent years, localization in a variety of Wireless Sensor Networks (WSNs) is a compelling but elusive goal. Several algorithms that use different methodologies have been proposed to achieve this goal. The performances of these algorithms depend on several factors, such as the sensor node placement, anchor deployment or network topology. In this paper, we propose a robust localization algorithm called Hybrid Efficient and Accurate Localization (HEA-Loc). HEA-Loc combines two techniques, Extended Kalman Filter (EKF) and Proximity-Distance Map (PDM) to improve localization accuracy. It is distributed in nature and works well in various scenarios as it is less susceptible to anchors deployment and the network topology. Furthermore, HEA-Loc has strong robustness and it can work well even the measurement errors are large. Simulation results show that HEA-Loc outperforms existing algorithms in both computational complexity and communication overhead. ©2010 IEEE.published_or_final_versionThe IEEE Wireless Communications and Networking Conference (WCNC 2010), Sydney, NSW., 18-21 April 2010. In Proceedings of WCNC, 2010, p. 1-

Epidemiological updates of venous thromboembolism in a Chinese population

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Chiral Spin Liquid in a Frustrated Anisotropic Kagome Heisenberg Model

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Effectiveness of proximal intra-operative salvage Palmaz stent placement for endoleak during endovascular aneurysm repair

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Angioplasty of forearm arteries as a finger salvage procedure for patient with end-stage renal failure

Case Reportpublished_or_final_versio

Low-dose versus high-dose fish oil for pain reduction and function improvement in patients with knee osteoarthritis

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Decentralization and marketization of education in China: challenges for migrant children and social harmony

Parallel Session II Governing the Asian Giants - Good Governance and Public Policy: Session on Managing Urban Development and Policy ResponsespostprintLaunch Conference for Journal of Asian Public Policy: "Governing the Asian Giants: The Search for Good Governance and Sustainable Development in China and India", Hong Kong, China, 29-30 March 2010