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数据挖掘2016:校准和收敛性的知识发现和HPC -托马斯英镑——印第安纳大学

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数据分析的多种形式已迅速扩展到交互科学、工业、社会应用领域。但随着越来越多的问题空间屈服于这类扩展的计算,对功能的需求扩大。同时,高性能计算(HPC)系统和方法正在经历重大变化在形式和性能与纳米半导体特征尺寸渐近收敛,因此摩尔定律即使exascale年底业绩预期在未来十年的早期。历史上这两个处理领域在很大程度上是独立的但现在越来越多的人达成共识推动起来,调整各自的形态和催化协同融合。大前提人总统行政命令导致国家战略计算计划规定,大量的数据和数字密集计算的合并是一个国家exascale宪章的成分。本课程将介绍许多系统架构和操作方法的转变将同时被要求回答的摩尔定律的挑战和图形处理方法,潜在的动态增强面向更传统的矩阵向量的计算。它将讨论可能的动态自适应资源管理和任务调度的重要性必须大幅提高可伸缩性和效率exascale计算机和这些变化将被应用到知识发现。回答今天的日益复杂和数据密集型科学问题在实验,观察和计算科学,我们正在开发方法在三个相互关联的研发领域:(i)我们在创建新的可扩展的数据分析方法能够运行在大规模计算平台应对日益复杂的科学探究。(2)我们的新计算关键设计模式分析方法将帮助科研人员充分利用计算技术的快速发展的趋势,例如增加每个处理器核心,更深层次的内存和存储层次结构和更复杂的计算平台。的关键目标是高性能和可移植性在能源部的计算平台。 (iii) By combining analysis and processing methods into data pipelines for use in large-scale HPC platforms—either standalone or integral to a larger scientific workflow—we are maximizing the opportunities for analyzing scientific data using a diverse collection of software tools and computational resources. Despite tremendous progress made in biological imaging that has yielded tomograms with ever-higher resolutions, the segmentation of cell tomograms into organelles and proteins remains a challenging task. The difficulty is most extreme in the case of cryo-electron tomography (cryo-ET), where the samples exhibit inherently low contrast due to the limited electron dose that can be applied during imaging before radiation damage occurs. The tomograms have a low signal-to-noise ratio (SNR), as well as missing-wedge artifacts caused by the limited sample tilt range that is accessible during imaging. While SNR can be improved by applying contrast enhancement and edge detection methods, these algorithms can also generate false connectivity and additional artifacts that degrade the results produced by automatic segmentation programs. If the challenges can be overcome, automatic segmentation approaches are of great interest. However, the achievement of this vision is precluded today by the complexity of the specimen and the SNR limitations described above. State of the art machine learning results are not generally suitable for deep mining, in fact, the situation in cryo-ET is quite the opposite: the highest quality segmentations are produced by hand, representing effort levels ranging from days to months. Segmentation tools could be vastly improved if they were constructed to take into account prior knowledge, minimizing the sensitivity to noise and false connection. To the best of our knowledge, there are no methods using specific contextual information about biological structures as restraints for segmentation. Nor are there approaches that incorporate active learning with feedback from the user, which would provide guidance as to the correctness of the segmentation. We are developing new machine learning techniques to facilitate the segmentation, extraction, visualization, and annotation of biological substructures within 3D tomograms obtained from a variety of imaging modalities.

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智能系统工程教授托马斯·斯特林是印第安纳大学信息学院和计算。他作为首席科学家和研究中心副主任Extreme Scale技术(峰值)。从麻省理工学院获得博士学位后在1984赫兹的家伙,他一直从事研究领域与并行计算系统结构和语义关联。他是6的书的作者之一,拥有6项专利。他获得了2013年的先锋奖。

托马斯英镑

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