The growing popularity of 3-D movies has led to the rapid development of numerous affordable consumer 3-D displays. In contrast, the development of technology to generate 3-D content has lagged behind considerably. In...
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(Figure Presented) Studying transformation in a chemical system by considering its energy as a function of coordinates of the system's components provides insight and changes our understanding of this process. Cur...
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A gradient clustering algorithm, based on the nonparametric methodology of statistical kernel estimators, expanded to its complete form, enabling implementation without particular knowledge of the theoretical aspects ...
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This WIP paper reports the status of a Windows based tool, 'Event Coding and visualization of data' (ECOVRD) that allows real time collaborative video annotation using Google App Engine (GAE) and XMPP protocol...
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This WIP paper reports the status of a Windows based tool, 'Event Coding and visualization of data' (ECOVRD) that allows real time collaborative video annotation using Google App Engine (GAE) and XMPP protocol. ECOVRD facilitates classification of live or video recorded individual and team behavior. It is designed with the dual purpose of advancing behavioral (observational) research and of supporting applied uses such as performance assessment and feedback during professional coaching. Users of ECOVRD can initiate a shared real-time video annotation with their social network (Google Buzz) via XMPP protocol. The custom-built publish/subscribe architecture wrapped around GAE's channel service, pushes data from the cloud to subscribed clients resulting in real-time collaborative experience. ECORVD may be the first to successfully leverage the server push framework of GAE for desktop based video annotation applications.
data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effe...
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ISBN:
(纸本)9783905674262
data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fit a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets. The Eurographics Association 2010.
Most analyses of ChIP-chip in vivo DNA binding have focused on qualitative descriptions of whether genomic regions are bound or not. There is increasing evidence, however, that factors bind in a highly overlapping man...
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Most analyses of ChIP-chip in vivo DNA binding have focused on qualitative descriptions of whether genomic regions are bound or not. There is increasing evidence, however, that factors bind in a highly overlapping manner to the same genomic regions and that it is quantitative differences in occupancy on these commonly bound regions that are the critical determinants of the different biological specificity of factors. As a result, it is critical to have a tool to facilitate the quantitative visualization of differences between transcription factors and the genomic regions they bind to understand each factor's unique roles in the network. We have developed a framework which combines several visualizations via brushing-and-linking to allow the user to interactively analyze and explore in vivo DNA binding data of multiple transcription factors. We describe these visualization types and also provide a discussion of biological examples in this paper.
Real-time beam predictions are desirable for ultrasound therapy guidance,as treatment planning requires individualized computations. To address thelong-standing issue of the computational burden associated with calcul...
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Real-time beam predictions are desirable for ultrasound therapy guidance, as treatment planning requires individualized computations. To address the long-standing issue of the computational burden associated with calc...
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data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effe...
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ISBN:
(纸本)9781617827242
data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fit a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets.
The quality of visual vocabularies is crucial for the performance of bag-of-words image classification methods. Several approaches have been developed for codebook construction, the most popular method is to cluster a...
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The quality of visual vocabularies is crucial for the performance of bag-of-words image classification methods. Several approaches have been developed for codebook construction, the most popular method is to cluster a set of image features (e.g. SIFT) by k-means. In this paper, we propose a two-step procedure which incorporates label information into the clustering process by efficiently generating a large and informative vocabulary using class-wise k-means and reducing its size by agglomerative information bottleneck (AIB). We introduce an extension of the AIB procedure for multi-label problems and show that this two-step approach improves the classification results while reducing computation time compared to the vanilla k-means. We analyse the reasons for the performance gain on the PASCAL VOC 2007 data set.
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