Measuring the Knowledge-Based Economy of China in terms of Synergy among Technological, Organizational, and Geographic Attributes of Firms

Using the possible synergy among geographic, size, and technological distributions of firms in the Orbis database, we find the greatest reduction of uncertainty at the level of the 31 provinces of China, and an additional 18.0% at the national level. Some of the coastal provinces stand out as expected, but the metropolitan areas of Beijing and Shanghai are (with Tianjan and Chonqing) most pronounced at the next-lower administrative level of (339) prefectures, since these four metropoles are administratively defined at both levels. Focusing on high- and medium-tech manufacturing, a shift toward Beijing and Shanghai is indicated, and the synergy is on average enhanced (as expected; but not for all provinces). Unfortunately, the Orbis data is incomplete since it was collected for commercial and not for administrative or governmental purposes. However, we show a methodology that can be used by others who may have access to higher-quality statistical data for the measurement.Comment: accepted for publication in Scientometrics (October, 2013

Automatic Image Classification for Planetary Exploration

Autonomous techniques in the context of planetary exploration can maximize scientific return and reduce the need for human involvement. This thesis work studies two main problems in planetary exploration: rock image classification and hyperspectral image classification. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. The proposed feature method is flexible and can outperform manually selected features. In order to take advantage of the unlabelled rock images, we also propose self-taught learning technique to learn the feature representation from unlabelled rock images and then apply the features for the classification of the subclass of rock images. Since combining spatial information with spectral information for classifying hyperspectral images (HSI) can dramatically improve the performance, we first propose an innovative framework to automatically generate spatial-spectral features for HSI. Two unsupervised learning methods, K-means and PCA, are utilized to learn the spatial feature bases in each decorrelated spectral band. Then spatial-spectral features are generated by concatenating the spatial feature representations in all/principal spectral bands. In the second work for HSI classification, we propose to stack the spectral patches to reduce the spectral dimensionality and generate 2-D spectral quilts. Such quilts retain all the spectral information and can result in less convolutional parameters in neural networks. Two light convolutional neural networks are then designed to classify the spectral quilts. As the third work for HSI classification, we propose a combinational fully convolutional network. The network can not only take advantage of the inherent computational efficiency of convolution at prediction time, but also perform as a collection of many paths and has an ensemble-like behavior which guarantees the robust performance

Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

Deep-learning frameworks have made remarkable progress thanks to the creation of large annotated datasets such as ImageNet, which has over one million training images. Although this works well for color (RGB) imagery, labeled datasets for other sensor modalities (e.g., multispectral and hyperspectral) are minuscule in comparison. This is because annotated datasets are expensive and man-power intensive to complete; and since this would be impractical to accomplish for each type of sensor, current state-of-the-art approaches in computer vision are not ideal for remote sensing problems. The shortage of annotated remote sensing imagery beyond the visual spectrum has forced researchers to embrace unsupervised feature extracting frameworks. These features are learned on a per-image basis, so they tend to not generalize well across other datasets. In this dissertation, we propose three new strategies for learning feature extracting frameworks with only a small quantity of annotated image data; including 1) self-taught feature learning, 2) domain adaptation with synthetic imagery, and 3) semi-supervised classification. ``Self-taught\u27\u27 feature learning frameworks are trained with large quantities of unlabeled imagery, and then these networks extract spatial-spectral features from annotated data for supervised classification. Synthetic remote sensing imagery can be used to boot-strap a deep convolutional neural network, and then we can fine-tune the network with real imagery. Semi-supervised classifiers prevent overfitting by jointly optimizing the supervised classification task along side one or more unsupervised learning tasks (i.e., reconstruction). Although obtaining large quantities of annotated image data would be ideal, our work shows that we can make due with less cost-prohibitive methods which are more practical to the end-user

Unsupervised Feature Selection Algorithm via Local Structure Learning and Kernel Function

In order to reduce dimensionality of high-dimensional data, a series of feature selection algorithms have been proposed. But these algorithms have the following disadvantages: (1) they do not fully consider the nonlinear relationship between data features (2) they do not consider the similarity between data features. To solve the above two problems, we propose an unsupervised feature selection algorithm based on local structure learning and kernel function. First, through the kernel function, we map each feature of the data to the kernel space, so that the nonlinear relationship of the data features can be fully exploited. Secondly, we apply the theory of local structure learning to the features of data, so that the similarity of data features is considered. Then we added a low rank constraint to consider the global information of the data. Finally, we add sparse learning to make feature selection. The experimental results show that the proposed algorithm has better results than the comparison methods

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Experience based action planning for environmental manipulation in autonomous robotic systems

The ability for autonomous robots to plan action sequences in order to manipulate their environment to achieve a specific goal is of vital importance for agents which are deployed in a vast number of situations. From domestic care robots to autonomous swarms of search and rescue robots there is a need for agents to be able to study, reason about, and manipulate their environment without the oversight of human operators. As these robots are typically deployed in areas inhabited and organised by humans it is likely that they will encounter similar objects when going about their duties, and in many cases the objects encountered are likely to be arranged in similar ways relative to one another. Manipulation of the environment is an incredibly complex task requiring vast amounts of computation to generate a suitable state of actions for even the simplest of tasks. To this end we explore the application of memory based systems to environment manipulation planning. We propose new search techniques targeted at the problem of environmental manipulation for search and rescue, and recall techniques aimed at allowing more complex planning to take place with lower computational cost. We explore these ideas from the perspective of autonomous robotic systems deployed for search and rescue, however the techniques presented would be equally valid for robots in other areas, or for virtual agents interacting with cyber-physical systems