Digital libraries and historical archives are increasingly employing visualization systems to facilitate the information retrieval and knowledge extraction tasks of their users. Typically, each organization employs a ...
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Digital libraries and historical archives are increasingly employing visualization systems to facilitate the information retrieval and knowledge extraction tasks of their users. Typically, each organization employs a single visualization system, which may not suit best the needs of certain user groups, specific tasks, or properties of document collections to be visualized. In this paper, we present a context-based adaptive visualization environment, which embeds a set of visualization methods into a visualization library, from which the most appropriate one is selected for presenting information to the user. Methods are selected by examining parameters related to the user profile, system configuration and the set of data to be visualized, and employing a set of rules to assess the suitability of each method. The presented environment additionally monitors user behavior and preferences to adapt the visualization method selection criteria
Although message passing using MPI is the dominant model for parallel programming today, the significant effort required to develop high-performance MPI applications has prompted the development of several parallel pr...
ISBN:
(纸本)9781424400546
Although message passing using MPI is the dominant model for parallel programming today, the significant effort required to develop high-performance MPI applications has prompted the development of several parallel programming models that are more convenient. Programming models such as Co-Array Fortran, Global Arrays, Titanium, and UPC provide a more convenient global view of the data, but face significant challenges in delivering high performance over a range of applications. It is particularly challenging to achieve high performance using global-address-space languages for unstructured applications with irregular data *** this paper, we describe a global-address-space parallel programming framework with decoupled task and data abstractions. The framework centers around the use of task pools, where tasks specify operands in a distributed, globally addressable pool of data chunks. The data chunks can be addressed in a logical multidimensional "tuple" space, and are distributed among the nodes of the system. Locality-aware load balancing of tasks in the task pool is achieved through judicious mapping via hyper-graph partitioning, as well as dynamic task/data migration. The framework implements a transparent interface for out-of-core data, so that explicit orchestration of movement of data between disks and memory is not required of the programmer. The use of the framework for implementation of parallel blocksparse tensor computations in the context of a quantum chemistry application is illustrated.
This paper presents a mobile and portable stereo vision system designed for the assessment of road signage and delineation (lines and reflective pavement markers or "cat's eyes"). This novel system allow...
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This paper presents a mobile and portable stereo vision system designed for the assessment of road signage and delineation (lines and reflective pavement markers or "cat's eyes"). This novel system allows both geometric and photometric measurements to be made on objects in a scene. Any objects examined can then be accurately positioned on a National grid through the fusion of stereo vision with GPS technology. Automated feature extraction and analysis routines have also been developed to make the system fully autonomous.
For the domain of biomedical research abstracts, two large corpora, namely GENIA (Kim et al 2003) and Penn BioIE (Kulik et al 2004) are available. Both are basically in human domain and the performance of systems trai...
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Fanconi Anemia (FA) is a rare autosomal genetic disease with multiple birth defects and severe childhood complications for its patients. The lack of sequence homology of the entire FA Complementation Group proteins in...
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ISBN:
(纸本)1595931082
Fanconi Anemia (FA) is a rare autosomal genetic disease with multiple birth defects and severe childhood complications for its patients. The lack of sequence homology of the entire FA Complementation Group proteins in such as FANCC, FANCG, FANCA makes them extremely difficult to characterize using conventional bioinformatics methods. In this work, we describe how to use computational methods to extract protein targets for FA, using protein interaction data set collected for FANC group C protein (FANCC). We first generated an initial set of 130 FA-interacting proteins as "FANCC seed proteins" by merging an in-house experimental set of FANCC Tandem Affinity Purification (TAP) Pulldown Proteomics data identified from Mass Spectrometry methods with publicly available human FANCC-interacting proteins. Next, we expanded the FANCC seed proteins using a nearest-neighbor method to generate a FANCC protein interaction subnetwork of 948 proteins in 903 protein interactions. We show that this network is statistically significant, with high indices of aggregation and separations. We also show a visualization of the network, support the evidence that many well-connected proteins exists in the network. Further, we developed and applied an interaction network protein scoring algorithm, which allows us to calculate a ranked list of significant FA proteins. Our result has been supporting further biological investigations of disease biologists on our team. We believe our method can be generalized to other disease biology studies with similar problems. Copyright 2006 ACM.
Delineation and reconstruction of curvilinear structures in medical images are critical for the diagnosis of various vascular diseases and related surgical procedures. In this paper, we present a novel method for vasc...
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ISBN:
(纸本)9780780395763
Delineation and reconstruction of curvilinear structures in medical images are critical for the diagnosis of various vascular diseases and related surgical procedures. In this paper, we present a novel method for vascular structure segmentation and reconstruction, including the automatic detection of bifurcation points. First, we perform a preselection of tubular structures. Second, we trace the vessels based on the eigenanalysis of the Hessian matrix. This provides us the estimated direction of vessels as well as the cross-sectional planes orthogonal to the vessels. A Scan-Conversion method is then applied to cross-sectional planes to automatically detect the bifurcation points of the vessels. This method has a 96.59% success rate for detecting bifurcation correctly. Finally, vessels are delineated and reconstructed using deformable models. Our method is efficient and allows for completely automatic delineation and reconstruction of vessels as well as automatic detection of bifurcation points.
Dirichlet Process (DP) mixture models are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to computational considerations these models are unfortunately unsuitabl...
Dirichlet Process (DP) mixture models are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to computational considerations these models are unfortunately unsuitable for large scale data-mining applications. We propose a class of deterministic accelerated DP mixture models that can routinely handle millions of data-cases. The speedup is achieved by incorporating kd-trees into a variational Bayesian algorithm for DP mixtures in the stick-breaking representation, similar to that of Blei and Jordan (2005). Our algorithm differs in the use of kd-trees and in the way we handle truncation: we only assume that the variational distributions are fixed at their priors after a certain level. Experiments show that speedups relative to the standard variational algorithm can be significant.
Cardiac beat classification is a key process in the detection of myocardial ischaemic episodes in the electrocardiographic (ECG) signal. In this, study we propose an automated methodology for the classification of car...
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This paper describes the development of an end-to-end quality measurement method that allows us to quantify the perceived quality of Interactive Gaming, with an emphasis on the so-called First Person Shooter (FPS) gam...
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ISBN:
(纸本)1595935894
This paper describes the development of an end-to-end quality measurement method that allows us to quantify the perceived quality of Interactive Gaming, with an emphasis on the so-called First Person Shooter (FPS) game Quake IV. We conducted a number of subjective experiments to quantify the impact of network parameters on the perceived quality of this recent FPS game. Making use of a multi-dimensional regression analysis we developed the Quake IV G-model which enables us to predict a gamer's Quake IV quality rating (expressed in a Mean Opinion Score) based on measured ping and jitter values. Our G-model shows a very high correlation (R = 0.98) with the subjective data. Copyright 2006 ACM.
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