Knowledge sources of innovation in a small open economy: The case of Singapore
By tracing the flows of patent citation to prior patents and scientific journal articles, we investigate the sources of knowledge for innovation output in Singapore, a small, highly open economy that has traditionally been significantly dependent on foreign multinational corporations (MNCs). We found that the local production of new knowledge by indigenous Singaporean firms depends disproportionately on technological knowledge produced by MNCs with operational presence in Singapore and scientific knowledge generated by foreign universities. Locally produced new knowledge by indigenous firms and local universities/ public research institutes constitutes an as yet insignificant, albeit growing, source for innovation in Singapore.

title: ICT production and diffusion in Asia Digital dividends or digital divide?
abstract: This paper examines the empirical evidence to determine whether Asian countries, despite having captured a disproportionately high share of global production of ICT goods, have as a group been laggard in the adoption of ICT in comparison to non-Asian countries. Using regression analysis, it is shown that as a group Asian countries have indeed had generally lower rates of ICT adoption relative to their levels of potential as predicted on the basis of their current level of development (GDP/capita) and competitiveness (world competitiveness index). In addition, disparities in ICT diffusion are found to be significantly higher among Asian countries than among non-Asian countries. In particular, a significant ‘digital divide’ is found to exist between the five more advanced countries of the region (Japan and the four Asian NIEs) and the other seven developing Asian countries. Policy implications of the findings for the Asian countries are highlighted.

title: Walking the Path: A New Journey to Explore and Discover Through Visual Analytics
abstract: Under the leadership of the US Department of Homeland Security (DHS), researchers at the Pacific Northwest National Laboratory (PNNL) established a research center focusing on the discipline of visual analytics in 2004. A year later, the center led a multidisciplinary panel representing academia, industry, and government to formally define directions and priorities for future research and development (R&D) for visual analytics tools. The R&D agenda, Illuminating the Path, defines the term visual analytics as ‘the science of analytical reasoning facilitated by interactive visual interfaces’. This article describes our progress to date in walking that path. We briefly describe the background of the subject, present major professional activities and accomplishments of its community, and highlight some of the ongoing R&D efforts being carried out by researchers at PNNL to fulfill the requirements and missions of a new discipline that promises to change the way we deal with today's information.

title: Data Signatures and Visualization of Scientific Data Sets
abstract: Today, as data sets used in computations grow in size and complexity, the technologies developed over the years to deal with scientific data sets have become less efficient and effective. Many frequently used operations, such as eigenvector computation, could quickly exhaust our desktop workstations once the data size reaches certain limits. On the other hand, the high-dimensional data sets we collect every day don't relieve the problem. Many conventional metric designs that build on quantitative or categorical data sets cannot be applied directly to heterogeneous data sets with multiple data types. While building new machines with more resources might conquer the data size problems, the complexity of today's computations requires a new breed of projection techniques to support analysis of the data and verification of the results. We introduce the concept of a data signature, which captures the essence of a scientific data set in a compact format, and use it to conduct analysis as if using the original. A time-dependent climate simulation data set demonstrates our approach and presents the results.

title: A capacity estimation technique for JPEG-to-JPEG image watermarking
abstract: In JPEG-to-JPEG image watermarking (J2J), the input is a JPEG image file. After watermark embedding, the image is JPEG-compressed such that the output file is also a JPEG file. We use the human visual system (HVS) model to estimate the J2J data hiding capacity of JPEG images, or the maximum number of bits that can be embedded in JPEG-compressed images.  Watson's HVS model (Proc. SPIE Human Vision, Visual Process., and Digital Display IV, p.202-16, 1993) is modified to estimate the just noticeable difference (JND) for DCT coefficients. The number of modifications to DCT coefficients is limited by JND in order to guarantee the invisibility of the watermark. Our capacity estimation method does not assume any specific watermarking method and thus would apply to any watermarking method in the J2J framework.

title: Discovering Knowledge Through Visual Analysis
abstract: This paper describes a vision for the near future in digital content analysis as it relates to the creation, verification, and presentation of knowledge. This paper focuses on how visualization enables humans to gain knowledge. Visualization, in this context, is not just the picture representing the data, but a two-way interaction between the human and their information resources for the purposes of knowledge discovery, verification, and the sharing of knowledge with others. We present PNNL-developed software to demonstrate how current technology can use visualization tools to analyze large diverse collections of text. This will be followed by lessons learned and the presentation of a core concept for a new human information discourse.

title: Predictive motion estimation for reduced-resolution video from high-resolution compressed video
abstract: To convert a compressed video sequence to a lower-resolution compressed video, one typically needs to decompress the original sequence, down-sample each frame, and recompress it. It involves motion estimation in the reduced sequence which is computational intensive. We propose a novel fast algorithm to predict the motion vector of the reduced video by using the original motion information in the compressed bitstream. We achieve a much higher quality than existing algorithms with low additional complexity.