45076304
Spatial Semantic Virtual Worlds: The Structural Duality and Beyond
982985
Visualizing and tracking the growth of competing paradigms: Two case studies
In this article we demonstrate the use of an integrative approach to visualizing and tracking the development of scientific paradigms. This approach is designed to reveal the long-term process of competing scientific paradigms. We assume that a cluster of highly cited and cocited scientific publications in a cocitation network represents the core of a predominant scientific paradigm. The growth of a paradigm is depicted and animated through the rise of citation rates and the movement of its core cluster towards the center of the cocitation network. We study two cases of competing scientific paradigms in the real world: (1) the causes of mass extinctions, and (2) the connections between mad cow disease and a new variant of a brain disease in humans — vCJD. Various theoretical and practical issues concerning this approach are discussed.
15274470
The rising landscape: A visual exploration of superstring revolutions in physics
Knowledge domain visualization is a visual exploratory approach to the study of the development of a knowledge domain. In this study, we focus on the practical issues concerning modeling and visualizing scientific revolutions. We study the growth patterns of specialties derived from citation and cocitation data on string theory in physics. Special attention is given to the two superstring revolutions since the 1980s. The superstring revolutions are visualized, animated, and analyzed using the general framework of Thomas Kuhn's structure of scientific revolutions. The implications of taking this approach are discussed.
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.
14518854
Detecting and mapping thematic changes in transient networks
Research fronts are the forefront of a scientific field timely identifying emerging trends and abrupt changes in scientific literature is not only beneficial for users of digital libraries, but also instrumental for users to trace the movement of a research front. A research front can be seen as a dynamic and transient mapping from the state of the art to its immediate prior art - the intellectual base. Citation and co-citation analysis primarily focuses on the structure and dynamics of the intellectual base, information retrieval and related research mainly focuses on intrinsic properties of text documents. In this article, we describe a novel approach that enables us to detect and visualize the transient relationship over time. Temporal associations between content words connected to a surge of interest in scientific papers and high-impact articles in the intellectual base are identified and visualized as a time-variant network. We apply the approach to the detection of thematic changes over time in information visualization and research in terrorism.
12098402
Noise-aware repeater insertion and wire-sizing for on-chip interconnect using hierarchical moment-matching
Recently, several algorithms for interconnect optimization via repeater insertion and wire sizing have appeared based on the Elmore delay model. Using the Devgan noise metric a noise-aware repeater insertion technique has also been proposed recently. Recognizing the conservatism of these delay and noise models, we propose a moment-matching based technique to interconnect optimization that allows for much higher accuracy while preserving the hierarchical nature of Elmore-delay-based techniques. We also present a novel approach to noise computation that accurately captures the effect of several attackers in linear time with respect to the number of attackers and wire segments. Our practical experiments with industrial nets indicate that the corresponding reduction in error afforded by these more accurate models justifies this increase in runtime for aggressive designs which is our targeted domain. Our algorithm yields delay and noise estimates within 5% of circuit simulation results.
27714693
Efficient Parallel Processing for K-Nearest-Neighbor Search in Spatial Databases
Even though the problem of k nearest neighbor (kNN) query is well-studied in serial environment, there is little prior work on parallel kNN search processing in parallel one. In this paper, we present the first Best-First based Parallel kNN (BFPkNN) query algorithm in a multi-disk setting, for efficient handling of kNN retrieval with arbitrary values of k by parallelization. The core of our method is to access more entries from multiple disks simultaneously and enable several effective pruning heuristics to discard non-qualifying entries. Extensive experiments with real and synthetic datasets confirm that BFPkNN significantly outperforms its competitors in both efficiency and scalability.
260704
An implementation of parallel dynamic load balancing for adaptive computing in VLSI device simulation
A new parallel semiconductor device simulation using the dynamic load balancing approach is presented. This semiconductor device simulation based on adaptive finite volume error estimation, and monotone iterative methods has been developed and implemented on a Linux-cluster with MPI library. Two different parallel versions of the algorithm to perform a complete device simulation are proposed. The first one is a dynamic parallel domain decomposition approach. The second version is a parallel current-voltage characteristic points simulation. The implementation shows that a well-designed load balancing simulation can reduce the execution time up to an order of magnitude. Compared with the measured data, numerical simulations on P-N diode, N-MOSFET, and DTMOS devices are presented to show the accuracy and efficiency of the method.
15500231
Image Copy Detection via Grouping in Feature Space Based on Virtual Prior Attacks
In the past, many researches on image copy detection focused on finding a feature that is robust enough for various kinds of image attacks. But it is difficult to find a globally effective feature that is appropriate for many situations. In this paper, we introduce a classification framework to this problem, instead of solving the feature-selection problem. In our approach, novel features are generated by applying virtual prior attacks to copyrighted images, and the copy-detection problem is converted to a classification one that is more robust to solve. Our approach can combine existing image copy detectors and further raise their performances.
596165
Combining models and guided empirical search to optimize for multiple levels of the memory hierarchy
This paper describes an algorithm for simultaneously optimizing across multiple levels of the memory hierarchy for dense-matrix computations. Our approach combines compiler models and heuristics with guided empirical search to take advantage of their complementary strengths. The models and heuristics limit the search to a small number of candidate implementations, and the empirical results provide the most accurate information to the compiler to select among candidates and tune optimization parameter values. We have developed an initial implementation and applied this approach to two case studies, matrix multiply and Jacobi relaxation. For matrix multiply, our results on two architectures, SGI R10000 and Sun UltraSparc IIe, outperform the native compiler, and either outperform or achieve comparable performance as the ATLAS self-tuning library and the hand-tuned vendor BLAS library. Jacobi results also substantially outperform the native compilers.