Scientific Computing Research Environments in the Mathematical Sciences (SCREMS)

摘要：John A Rice提案：0322751此赠款的支持使加州大学伯克利分校的统计系能够购买一个包含四个PowerEdge双处理器的Linux计算机服务器群集、一个660 GB的StorEdge磁盘驱动器机架和一个20盒机架式磁带库。 该设备专用于支持计算密集型应用统计研究。 特别是，研究人员和同事们解决了公路运输中的问题，例如使用高速公路时空段的视频数据校准驾驶行为的微观模型，这些视频数据来自道路上方，这是一项需要新型计算机视觉算法的任务。他们使用基因表达微阵列数据研究基因组学和统计遗传学中的问题，例如差异表达基因的鉴定，肿瘤和其他细胞类型的分类，以及基因调控机制的阐明。他们将时间过程网络应用于神经科学，并扩展了最近的机器学习预测方法，并将结果应用于使用多角度卫星图像数据的冰雪云检测。该资助支持的研究特别关注科学项目，这些项目共同需要计算密集型统计方法来分析大型数据集。 这种研究需要计算设施，这与生成数据集的设备的速度和存储容量相匹配。该补助金将允许该部门购买适当的处理器和数据存储设备，以允许进行这项研究。研究人员及其同事使用该设备分析州际公路上交通流量的视频记录，以便将驾驶员行为的微观模型与经验数据进行比较，最终目标是更好地管理公路交通。他们利用过去的森林火灾数据来预测未来火灾的可能性，这些火灾可能性是位置、过去的火灾历史、气象变量、燃烧指数和其他相关变量的函数。他们开发用于分析收集的基因表达数据的统计方法，以改善癌症诊断，了解与疾病易感性相关的遗传变化，并为细胞生长和发育的基础研究做出贡献。 他们利用多角度卫星图像数据开发预测冰雪上云层存在的方法，从而改进天气预测和全球变暖监测。 这些例子只是科学研究的一个例子，由于赠款支持而购买的设备使科学研究变得可行。

ABSTRACTPI: John A RiceProposal: 0322751The support from this grant allows the Department of Statistics at the University of California, Berkeley to purchase a Linux computer-server cluster of four PowerEdge dual processors, a 660 gigabyte StorEdge rack of disk drives, and a 20 cartridge rack mount tape library. This equipment is dedicated to the support of computationally intensive applied statistical research. In particular, the investigators and colleagues tackle problems in highway transportation, such as the calibration of micro models of driving behavior using video data of spatio-temporal segments of highways, taken from high above the roadway, a task requiring novel computer vision algorithms. They study questions in genomics and statistical genetics using gene expression microarray data, such as the identification of differentially expressed genes, the classification of tumors and other cell types, and the elucidation of gene regulatory mechanisms. They apply networks of temporal processes to neuroscience, and extend recent machine learning prediction methods and apply the results to cloud detection over ice and snow using multi-angle satellite image data.The research supported by this grant focuses specifically on projects in science sharing a common need for computationally intensive statistical methods to analyze large datasets. Such research requires computing facilities, which match the speed and storage capacity of the devices generating the datasets. The grant is to allow the Department to purchase appropriate processors and data storage devices to permit conduct of this research. The investigators and colleagues use this equipment to analyze video-recordings of traffic flow on interstate highways, in order to compare microscopic models of driver behavior with empirical data, the ultimate aim being better management of highway traffic. They use past data on forest fires to predict the likelihood of future fires as a function of location, past fire history, meteorological variables, burning indices, and other relevant variables. They develop statistical methods for the analysis of gene expression data collected to improve cancer diagnosis, to understand the genetic changes associated with disease susceptibility, and to contribute to basic research on cellular growth and development. They use multi-angle satellite image data to develop methods of predicting the presence of cloud over snow and ice, thereby permitting improved weather prediction and global warming monitoring. These examples constitute but a sample of the scientific research, which become feasible with the equipment purchased as the result of the grant support.