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2018极限科学研讨会（Workshop on Science at Extreme Scales: Where Big Data Meets Large-Scale Computing Tutorials）
2018 极限科学 研讨会
2017/12/20
The program opens with four days of tutorials that will provide an introduction to major themes of the entire program and the four workshops. The goal is to build a foundation for the participants of...
Increasingly large data sets are being ingested and produced by simulations. What experience from large-scale simulation is transferable to big data applications? Conversely, what new optimal algorith...
BROADBAND SENSOR LOCATION SELECTION USING CONVEX OPTIMIZATION IN VERY LARGE SCALE ARRAYS
array processing multi-frequency beam pattern design sensor location selection very large scale arrays convex optimization simulated annealing
2015/9/29
Consider a sensing system using a large number of N microphones placed in multiple dimensions to monitor a broadband acoustic field. Using all the microphones at once is impractical because of the amo...
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso
sparse inverse covariance selection sparsity graphical lasso Gaussian graphical models graph connected components concentration graph large scale covariance estimation
2015/8/21
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter λ. Suppose the sample covariance graph formed by thresholding the entries of the sampl...
Selection and Estimation for Large-Scale Simultaneous Inference
Large-Scale Simultaneous Inference Selection
2015/8/20
Modern scientific technology is providing a new class of simultaneous inference
problems for the applied statistician, where there are hundreds or thousands or even
more hypothesis tests to co...
Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis
Null Hypothesis Simultaneous Hypothesis Testing
2015/8/20
Current scientific techniques in genomics and image processing routinely produce hypothesis testing problems with hundreds or thousands of cases to consider simultaneously.
This poses new di...
Correlated z-values and the accuracy of large-scale statistical estimates
large-scale statistical estimates Correlated z-values
2015/8/20
We consider large-scale studies in which there are hundreds or thousands of correlated cases to investigate, each represented by its own normal variate, typically a
z-value. A familiar example is pro...
Empirical Bayes Estimates for Large-Scale Prediction Problems
microarray prediction empirical Bayes shrunken centroids
2015/8/20
Classical prediction methods such as Fisher's linear discriminant function were designed for
small-scale problems, where the number of predictors N is much smaller than the number of
observations n....
We give an algorithm that computes the final state of certain growth models without computing all intermediate states. Our technique is based on a “least action principle” which characterizes the odom...
An efficient method for large-scale slack allocation
timing graph slack allocation delay budgeting convex optimization truncated Newton method
2015/8/7
We consider a timing or project graph, with given delays on the edges and given arrival times at the source and sink nodes. We are to find the arrival times at the other nodes; these determine the tim...
Large scale behaviour of the spatial Lambda-Fleming-Viot process
spatial Lambda-Fleming-Viot process Large scale behaviour Probability
2011/9/16
Abstract: We consider the spatial Lambda-Fleming-Viot process model for frequencies of genetic types in a population living in R^d, in the special case in which there are just two types of individual,...
Towards a Better Understanding of Large Scale Network Models
Better Understanding Large Scale Network Models
2011/3/4
Connectivity and capacity are two fundamental properties of wireless multi-hop networks. The scalability of these properties has been a primary concern for which asymptotic analysis is a useful tool.
Large-scale interval and point estimates from an empirical Bayes extension of confidence posteriors
Large-scale interval point estimates empirical Bayes extension confidence posteriors
2011/3/4
In statistical genomics, bioinformatics, and neuroinformatics, truth values of multiple
hypotheses are often modeled as random quantities of a common mixture distribution in order to estimate false d...
Large-scale interval and point estimates from an empirical Bayes extension of confidence posteriors
Large-scale interval point estimates empirical Bayes
2011/1/4
The proposed approach extends the confidence posterior distribution to the semi-parametric empirical Bayes setting. Whereas the Bayesian posterior is defined in terms of a prior distribution condition...
Price decomposition in large-scale stochastic optimal control
Stochastic optimal control Decomposition methods
2011/1/20
We are interested in optimally driving a dynamical system that can be influenced by exogenous noises. This is generally called a Stochastic Optimal Control (SOC) problem and the Dynamic Programming (D...