Data Driven Discovery in Astrophysics

We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" science, with exponentially growing data volumes and data rates, and an ever-increasing complexity, now entering the Petascale regime. Telescopes and observatories from both ground and space, covering a full range of wavelengths, feed the data via processing pipelines into dedicated archives, where they can be accessed for scientific analysis. Most of the large archives are connected through the Virtual Observatory framework, that provides interoperability standards and services, and effectively constitutes a global data grid of astronomy. Making discoveries in this overabundance of data requires applications of novel, machine learning tools. We describe some of the recent examples of such applications.Comment: Keynote talk in the proceedings of ESA-ESRIN Conference: Big Data from Space 2014, Frascati, Italy, November 12-14, 2014, 8 pages, 2 figure

CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

Driven by the wave of urbanization in recent decades, the research topic about migrant behavior analysis draws great attention from both academia and the government. Nevertheless, subject to the cost of data collection and the lack of modeling methods, most of existing studies use only questionnaire surveys with sparse samples and non-individual level statistical data to achieve coarse-grained studies of migrant behaviors. In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels. Specifically, CD-CNN features in decomposing the mobile data into location domain and communication domain, and adopts a joint learning framework that combines two convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN employs a three-step algorithm for training, in which the co-training step is of great value to partially supervised cross-domain learning. Comparative experiments on the city Wuxi demonstrate the high predictive power of CD-CNN. Two interesting applications further highlight the ability of CD-CNN for in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc

Some Pattern Recognition Challenges in Data-Intensive Astronomy

We review some of the recent developments and challenges posed by the data analysis in modern digital sky surveys, which are representative of the information-rich astronomy in the context of Virtual Observatory. Illustrative examples include the problems of an automated star-galaxy classification in complex and heterogeneous panoramic imaging data sets, and an automated, iterative, dynamical classification of transient events detected in synoptic sky surveys. These problems offer good opportunities for productive collaborations between astronomers and applied computer scientists and statisticians, and are representative of the kind of challenges now present in all data-intensive fields. We discuss briefly some emergent types of scalable scientific data analysis systems with a broad applicability.Comment: 8 pages, compressed pdf file, figures downgraded in quality in order to match the arXiv size limi