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In this paper, we formulate a novel graph-based learning problem, multi-graph classification (MGC), which aims to learn a classifier from a set of labeled bags each containing a number of graphs inside the bag. A bag is labeled positive, if at least one graph in the bag is positive, and negative otherwise. Such a multi-graph representation can be used for many real-world applications, such as webpage...
Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to have useful information from different channels/views to describe objects, which naturally results in a new representation with multiple graphs generated from different feature views being used to describe one object. In this paper, we formulate a new Multi-Graph-View...
Multi-instance learning concerns about building learning models from a number of labeled instance bags, where each bag consists of instances with unknown labels. A bag is labeled positive if one or more multiple instances inside the bag is positive, and negative otherwise. For all existing multi-instance learning algorithms, they are only applicable to the setting where instances in each bag are represented...
This paper reports on high-quality and controlled growth of single crystalline InN nanowires (NWs) synthesized by chemical vapor deposition (CVD) technique. Growth rate as high as 30 ??m/hour was achieved by the vapor-liquid-solid (VLS) mechanism in a horizontal tube furnace. The NWs bent spontaneously, or were deflected by other NWs or patterned SiO2 barrier at angles multiples of 30??. Also the...
In this paper, utilized scanning current voltage microscopy (SVIM) techniques was used to investigate charge transport and trapping in the individual nanowires InN (NWs) and nanonetworks. SVIM images indicate non-uniform surface barrier height on NWs, strongly affected by surface deformations.The Ip-Vp measurements indicate type-II heterostructure band alignment causing unusually large probe current...
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