468820
Tracing knowledge diffusion
Knowledge diffusion is the adaptation of knowledge in a broad range of scientific and engineering research and development. Tracing knowledge diffusion between science and technology is a challenging issue due to the complexity of identifying emerging patterns in a diverse range of possible processes. In this article, we describe an approach that combines complex network theory, network visualization, and patent citation analysis in order to improve the means for the study of knowledge diffusion. In particular, we analyze patent citations in the field of tissue engineering. We emphasize that this is the beginning of a longer-term endeavor that aims to develop and deploy effective, progressive, and explanatory visualization techniques for us to capture the dynamics of the evolution of patent citation networks. The work has practical implications on resource allocation, strategic planning, and science policy.
15424500
Semantically modified diffusion limited aggregation for visualizing large-scale networks
Diffusion-limited aggregation (DLA) is a model of fractal growth. Computer models can simulate the fast aggregation of millions of particles. We propose a modified version of DLA, called semantically modified DLA (SM-DLA), for visualizing large-scale networks. SM-DLA introduces similarity measures between particles so that instead of attaching to the nearest particle in the aggregation, a new particle is stochastically directed to attach to particles that are similar to it. The results of our initial experiment with a co-citation network using SM-DLA are encouraging, suggesting that the algorithm has the potential as an alternative paradigm for visualizing large-scale networks. Further studies in this direction are recommended.
42451139
Visualizing scientific paradigms: An introduction
This special topic issue includes a collection of seven articles on visualizing scientific paradigms. All articles in this special issue reflect the influence of Thomas Kuhn’s structure of scientific revolutions on our understanding of the growth of scientific knowledge. On the other hand, each article represents a unique perspective of how one may pursue the quest for transforming something as intangible and empirically evasive as invisible colleges and competing paradigms into something that is more accessible and traceable to scholars and professions of various disciplines, ranging from historians, philosophers, and educators to scientists and engineers. The opening article is Henry Small’s personal account of the endeavor in probing scientific paradigms using science mapping. His article, entitled “Paradigms, Citations and Maps of Science: A Personal History,” provides a valuable introduction to the key issues involved in this area and reveals an overview of promising avenues as well as challenging routes. He identifies an emerging trend that reflects and redefines Kuhn’s paradigms and emphasizes the empirical viability of research methodologies in understanding the growth of scientific knowledge. He suggests that revolutionary and normal science should be seen as extremes on a continuum of rates of change rather than, as Kuhn originally asserted, as an all-or-none proposition. The second and third articles take a prominent historical perspective in depicting the evolution of scientific citations. Eugene Garfield and his collaborators’ article is entitled “Why Do We Need Algorithmic Historiography?” They explain the rationale for creating HistCite, their new program that produces historiographs of scholarly topics. These historiographs help analysts and historians identify key events, people, and journals in a particular field. Scholars can evaluate the evolution of a scientific paradigm by studying the genealogic profile of such evolution. The third article is entitled “Timeline Visualization of Research Fronts.” Steven Morris and his colleagues focus on temporal changes in research fronts and explain the power of timeline visualizations. They first cluster documents into research fronts based on bibliographic coupling and then visualize research fronts using horizontal timeline tracks. Documents from research fronts are plotted along corresponding timeline tracks. Because time line visualizations can reveal temporal relations among research fronts and their member documents, time lines are potentially useful tools in identifying new research fronts as well as existing research expertise and research excellence. Although both the fourth and fifth articles have connections to Pathfinder networks, they address different topics. Howard White’s article, entitled “Pathfinder Networks and Author Cocitation Analysis: A Remapping of Paradigmatic Information Scientists,” clarifies an important methodological issue, whereas Chen and Kuljis focus on a general framework for visualizing scientific paradigms in their article. Author Cocitation Analysis (ACA) is a long established methodology for studying intellectual structures. Researchers have routinely used Pearson correlation coefficients rather than raw cocitation counts as the measure of similarity between author pairs. Howard White compellingly demonstrates the advantages of using raw cocitation counts in Pathfinder network visualizations of author cocitation structures. He compares Pathfinder networks of the information scientists derived from raw cocitation counts and Pearson correlation coefficients, respectively. In the fifth article “The Rising Landscape: A Visual Exploration of Superstring Revolutions in Physics,” Chen and Kuljis introduce a general framework for visualizing the growth of scientific paradigms based on Kuhn’s theory on scientific revolutions. They illustrate the use of this framework in a case study of superstring revolutions in physics. The evolution of cocitation networks is depicted and explained through animated visualizations of citations and cocitation links. Visual-spatial properties as well as structural patterns associated with superstring revolutions are identified. The sixth and seventh articles deal with heterogeneous data sources. In their article entitled “Indicator-Assisted Evaluation and Funding of Research: Visualizing the Influ© 2003 Wiley Periodicals, Inc.
61209915
CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature
This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature—an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981–2004) and terrorism (1990–2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified. © 2006 Wiley Periodicals, Inc.
2965625
Visualizing the evolution of a subject domain: a case study
We explore the potential of information visualization techniques in enhancing existing methodologies for domain analysis and modeling. In this case study, we particularly focus on visualizing the evolution of the hypertext field based on author co-citation patterns, including the use of a sliding-window scheme to generate a series of annual snapshots of the domain structure, and a factor-referenced color-coding scheme to highlight predominant specialties in the field.
45076304
Spatial Semantic Virtual Worlds: The Structural Duality and Beyond
2319896
Range Image Registration Based on Circular Features
Range sensing technology allows the photo-realistic modeling of large-scale scenes, such as urban structures. A major bottleneck in the process of 3D scene acquisition is the automated registration of a large number of geometrically complex 3D range scans in a common frame of reference. The generated 3D representations, after automated registration, are useful for urban planning, historical preservation, or virtual reality applications. Man-made urban scenes provide an abundance of linear features that can be used for the solution of the problem. Many scenes though require the utilization of non-linear primitives. This paper provides a solution of the registration problem based on the robust detection and matching of circular features from the range data sets. We present results from experiments with complex range scans from the interior of a large-scale landmark urban structure (Grand Central Terminal, NYC), where traditional methods would fail. This work is part of a larger range registration system that is based on extracted features of multiple geometric types.
393866
Adaptive discretization for probabilistic model building genetic algorithms
This paper proposes an adaptive discretization method, called Split-on-Demand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization problems in the continuous domain. The procedure, effect, and usage of SoD are described in detail. As an example, the integration of SoD and the extended compact genetic algorithm (ECGA), named real-coded ECGA (rECGA), is presented and numerically examined. The experimental results indicate that rECGA works well and SoD is effective. The behavior of SoD is analyzed and discussed, followed by the potential future work for SoD.
6671949
Identification of critical genes in microarray experiments by a Neuro-Fuzzy approach
Gene expression profiling by microarray technology is usually difficult to interpret into a simpler pattern. One approach to resolve the complexity of gene expression profiles is the application of artificial neural networks (ANNs). A potential difficulty in this strategy, however, is that the non-linear nature of ANN makes it essentially a 'black-box' computation process. Addition of a fuzzy logic approach is useful because it can complement ANN by explicitly specifying membership function during computation. We employed a hybrid approach of neural network and fuzzy logic to further analyze a published microarray study of gene responses to eight bacteria in human macrophages. The original analysis by hierarchical clustering found common gene responses to all bacteria but did not address individual responses. Our method allowed exploration of the gene response of the host to individual bacterium. We implemented a two-layer, feed-forward neural network containing the principle of 'competitive learning' (i.e. 'winner-take-all'). The weights of the trained neural network were fed into a fuzzy logic inference system. A new measurement, called the impact rating (IR) was also introduced to explore the degree of importance of each gene. To assess the reliability of the IR value, a bootstrap re-sampling method was applied to the dataset and a confidence level for each IR was obtained. Our approach has successfully uncovered the unique features of host response to individual bacterium. Further, application of gene ontology (GO) annotation to the genes of high IR values in each response has suggested new biological pathways for individual host-pathogen interactions.
1491961
WormBase: a multi-species resource for nematode biology and genomics
WormBase (http://www.wormbase.org/) is the central data repository for information about Caenorhabditis elegans and related nematodes. As a model organism database, WormBase extends beyond the genomic sequence, integrating experimental results with extensively annotated views of the genome. The WormBase Consortium continues to expand the biological scope and utility of WormBase with the inclusion of large-scale genomic analyses, through active data and literature curation, through new analysis and visualization tools, and through refinement of the user interface. Over the past year, the nearly complete genomic sequence and comparative analyses of the closely related species Caenorhabditis briggsae have been integrated into WormBase, including gene predictions, ortholog assignments and a new synteny viewer to display the relationships between the two species. Extensive site-wide refinement of the user interface now provides quick access to the most frequently accessed resources and a consistent browsing experience across the site. Unified single-page views now provide complete summaries of commonly accessed entries like genes. These advances continue to increase the utility of WormBase for C.elegans researchers, as well as for those researchers exploring problems in functional and comparative genomics in the context of a powerful genetic system.
14158254
Smoothing methods for convex inequalities and linear complementarity problems
A smooth approximationp (x, α) to the plus function max{x, 0} is obtained by integrating the sigmoid function 1/(1 + e−αx), commonly used in neural networks. By means of this approximation, linear and convex inequalities are converted into smooth, convex unconstrained minimization problems, the solution of which approximates the solution of the original problem to a high degree of accuracy forα sufficiently large. In the special case when a Slater constraint qualification is satisfied, an exact solution can be obtained for finiteα. Speedup over MINOS 5.4 was as high as 1142 times for linear inequalities of size 2000 × 1000, and 580 times for convex inequalities with 400 variables. Linear complementarity problems are converted into a system of smooth nonlinear equations and are solved by a quadratically convergent Newton method. For monotone LCPs with as many as 10 000 variables, the proposed approach was as much as 63 times faster than Lemke's method.