Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this paper a novel data-synthesized spatio-temporally convolutional encoder-decoder network method termed DST-CedNet is proposed for ESI. DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a convolutional encoder-decoder network (CedNet) to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and Epilepsy EEG dataset demonstrate that DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.Comment: 15 pages, 14 figures, and journa

The governance of urban energy transitions: A comparative study of solar water heating systems in two Chinese cities

This paper examines how urban energy transitions are unfolding in China, in relation to the deployment of solar water heating (SWH) systems in two Chinese cities, Rizhao and Shenzhen. Cities play a significant role in the energy transition in China. Scholarly efforts have looked into the translation of top-down visions into locally actionable policy. This article contributes to this body of research with an analysis of the urban governance of urban energy transitions in China, and how low carbon technologies are deployed in particular urban contexts. The comparative analysis of Rizhao and Shenzhen suggests that specific socio-spatial arrangements shape the evolutionary trajectories of urban energy transitions of SWH systems in both cities. In the case of Rizhao, policy approaches have been erratic. Nevertheless, governmental and civil society actors have worked to forge alignment among political visions, built environment constraints, and social practices. The proximity of an industrial cluster supporting SWH technology and the early uptake of this technology by households are two key factors that explain the rapid spread of SWH systems in Rizhao. In Shenzhen, the local government has promoted SWH systems through regulation and incentives in a top-down and coordinated manner. These programmes have been, however, abandoned, after they did not deliver the expected results. The two contrasting cases suggest that the urban energy transition in China is the result of the coordinated actions of multiple actors, and success depends on the fit between technologies and the urban development contexts, rather than on aggressive government-sponsored actions

The strong chromatic index of (3,Δ)-bipartite graphs

A strong edge-coloring of a graph G=(V,E) is a partition of its edge set E into induced matchings. We study bipartite graphs with one part having maximum degree at most 3 and the other part having maximum degree Δ. We show that every such graph has a strong edge-coloring using at most 3Δ colors. Our result confirms a conjecture of Brualdi and Quinn Massey (1993) for this class of bipartite graphs

The strong chromatic index of (3,Δ)-bipartite graphs

A strong edge-coloring of a graph G=(V,E) is a partition of its edge set E into induced matchings. We study bipartite graphs with one part having maximum degree at most 3 and the other part having maximum degree Δ. We show that every such graph has a strong edge-coloring using at most 3Δ colors. Our result confirms a conjecture of Brualdi and Quinn Massey (1993) for this class of bipartite graphs

The strong edge-coloring for graphs with small edge weight

A strong edge-coloring of a graph G=(V,E) is a partition of its edge set E into induced matchings. The edge weight of a graph G is defined to be max{dG(u)+dG(v)|e=uv∈E(G)}. We study graphs with edge weight at most 7. We show that 1) every graph with edge weight at most 6 has a strong edge-coloring using at most 10 colors; and 2) every graph with edge weight at most 7 has a strong edge-coloring using at most 15 colors

Natural Sciences Publishing Cor. A Trust Evaluation Mechanism for Collaboration of Data-Intensive Services in Cloud

Abstract: Trust and reputation for services emerges as an important issue in cloud computing. Since data-intensive services in cloud have been used in more and more fields, trust evaluation for collaboration of services meets more challenges. There are not only logical dependencies but also data dependencies among partner services when data-intensive services take part in collaboration. This paper proposes a novel trust evaluation method for collaborations of data-intensive services. It considers not only the trust for individual partner services and the explicit trust relation among partner services that have logical dependencies for each other, but also the implicit trust relation implied in data-dependencies among services. A serial of experiments, using the simulation tool NetLogo, are carried out to compare the evaluation results between the proposed method and the method without data-dependency consideration. The result shows that taking consideration of the data-dependency trust improves the accuracy of trust evaluation to a great extent