In highly adaptive irregular problems such as many Particle-In-Cell (PIC) codes and Direct Simulation Monte Carlo (DSMC) codes, data access patterns may vary from time step to time step. This uctuation may hinder eecient utilization of distributed memory parallel computers because of the resulting overhead for data redistribution and dynamic load balancing. To eeciently parallelize such adap-ti...

This paper presents the colored farthest-neighbor graph (CFNG), a new method for finding clusters of similar objects. The method is useful because it works for both objects with coordinates and for objects without coordinates. The only requirement is that the distance between any two objects be computable. In other words, the objects must belong to a metric space. The CFNG uses graph coloring t...

Three multivariate methods for predicting death within 1 year for patients discharged after acute myocardial infarction were evaluated: Cox model, discriminant function analysis and recursive partitioning. Discriminant function analysis was utilized to predict a new myocardial infarction (any new or nonfatal infarction). A Cox classification model developed in a population of 260 patients (grou...

Assessing vegetation water content (VWC) from hyperspectral reflectance dataset poses two foremost questions: what specific wavebands of the SWIR offer a good retrieval and what modeling methods have the best predictive ability. In this paper, we explored the application of multivariate statistical techniques such as stepwise multiple linear regression (SMLR) and partial least square regression...

More and more vector-based cellular automata (VCA) models have been built to leverage parallel computing to model rapidly changing cities and urban regions. During parallel simulation, common task decomposition methods based on space partitioning, e.g., grid partitioning (GRID) and recursive binary space partitioning (BSP), do not work well given the heterogeneity of VCA parcel tasks. In this p...

Greedy search or exhaustive search plays a critical role in recursive partitioning and their extensions. We examine an alternative method by replacing the indicator threshold function involved in recursive partitioning with a smooth sigmoid surrogate (SSS) function. In many scenarios, it is important to formulate the problem appropriately, which can change the discrete greedy search for the bes...

ÐIn this paper, a novel method of pattern discovery is proposed. It is based on the theoretical formulation of a contingency table of events. Using residual analysis and recursive partitioning, statistically significant events are identified in a data set. These events constitute the important information contained in the data set and are easily interpretable as simple rules, contour plots, or ...

In highly adaptive irregular problems such as many Particle-In-Cell (PIC) codes and Direct Simulation Monte Carlo (DSMC) codes, data access patterns may vary from time step to time step. This uctuationmay hinder e cient utilization of distributed memory parallel computers because of the resulting overhead for data redistribution and dynamic load balancing. To e ciently parallelize such adaptive...

In the present work the issue of decomposing a configuration problem is approached in a framework where the domain knowledge is represented in a structured way by using a KL-One like language and whole-part relations play a major role in defining the structure of the configurable objects. The representation formalism provides also a constraint language for expressing complex relations among com...

We propose a novel Dynamic Recursive Partitioning approach for discovering discriminative patterns of functional MRI activation. The goal is to efficiently identify spatial regions that are associated with non-spatial variables through adaptive recursive partitioning of the 3D space into a number of hyperrectangles utilizing statistical tests. As a case study, we analyze fMRI datasets obtained ...