Urban Panorama Tourism Planning A View From River Tour Course In Post-Three Gorges Era

The upstream cities of Yangtze River have been witnessing significant transforming since the beginning of the construction of the Three Gorges hydroelectric dam project. Chongqing Port Authority had its opportunity to alternate the river tourism strategy from being the upstream terminal of the golden route into creating a particular cruise course towards perceiving the panorama of continuous elevation of mountainous city, at the same time, promoting the renovation of the urban design so as to revival the typical mountain-river vista. This paper bases on the panoramic research of Chongqing peninsula; discusses the characteristic aspects of the three-dimension sightseeing of the mountainous city on the cruise route, which widely exists in the Three Gorges region as well. And this method is different from the two dimensional approach of skyline analysis which is more suitable for the topographic area. The achieved work can offer the tourism-related sectors a sustainable assistance to deal with “tourbanism” topics in the urban regeneration process in the Three Gorges regions.Las ciudades situadas a lo largo del río Yangtze han sido testigo de cambios significativos desde los inicios de la construcción de la Presa hidroeléctrica de las Tres Gargantas. La autoridad portuaria de Chongqing tuvo la oportunidad de alternar la estrategia turística del río, pasando de ser una parada de la ruta dorada en lo alto del río a crear una ruta navegable particular de cara a la visualización del panorama de la ciudad montañosa, promoviendo, a su vez, la renovación del diseño urbanístico para revivir la vista típica del paisaje de río y montaña. Este artículo se basa en la investigación panorámica de la península de Chongqing; trata los aspectos característicos de las tres dimensiones del turismo de la ciudad montañosa en la ruta navegable, que existe también en la región de las Tres gargantas. Este método es diferente del acercamiento bidimensional de análisis del skyline, más propicio para el área topográfica. El trabajo conseguido puede ofrecer a los sectores relacionados con el turismo asistencia sostenible para lidiar con los temas de “tourbanism” en el proceso de regeneración urbana las regiones de las Tres Gargantas

Urban panorama tourism planning: a view from river tour course in post-three gorges era

The upstream cities of Yangtze River have been witnessing significant transforming since the beginning of the construction of the Three Gorges hydroelectric planet project. Chongqing Port Authority had its opportunity to alternate the river tourism strategy from being the upstream terminal of the golden route into creating a particular cruise course towards perceiving the panorama of continuous elevation of mountainous city, at the same time, promoting the renovation of the urban design so as to revival the typical mountain-river vista. This paper bases on the panoramic research of Chongqing peninsula; discusses the characteristic aspects of the three-dimension sightseeing of the mountainous city on the cruise route, which widely exists in the Three Gorges region as well. And this method is different from the two dimensional approach of skyline analysis which is more suitable for the topographic area. The achieved work can offer the tourism-related sectors a sustainable assistance to deal with “tourbanism” topics in the urban regeneration process in the Three Gorges regions.Peer Reviewe

Large-Scale Multi-Label Learning with Incomplete Label Assignments

Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing labels. However, in many real-world multi-label datasets, the label assignments for training instances can be incomplete. Some ground-truth labels can be missed by the labeler from the label set. This problem is especially typical when the number instances is very large, and the labeling cost is very high, which makes it almost impossible to get a fully labeled training set. In this paper, we study the problem of large-scale multi-label learning with incomplete label assignments. We propose an approach, called MPU, based upon positive and unlabeled stochastic gradient descent and stacked models. Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data. Extensive experiments on two real-world multi-label datasets show that our MPU model consistently outperform other commonly-used baselines