Reconstruction of the Central Layer of the Human Cerebral Cortex from MR Images
Reconstruction of the human cerebral cortex from MR images is a fundamental step in human brain mapping and in applications such as surgical path planning. In a previous paper, we described a method for obtaining a surface representation of the central layer of the human cerebral cortex using fuzzy segmentation and a deformable surface model. This method, however, suffers from several problems. In this paper, we significantly improve upon the previous method by using a fuzzy segmentation algorithm robust to intensity inhomogeneities, and using a deformable surface model specifically designed for capturing convoluted sulci or gyri. We demonstrate the improvement over the previous method both qualitatively and quantitatively, and show the result of its application to six subjects. We also experimentally validate the convergence of the deformable surface initialization algorithm.

title: A 2D moving grid geometric deformable model
abstract: Geometric deformable models based on the level set method have become very popular. To overcome an inherent limitation in accuracy while maintaining computational efficiency, adaptive grid techniques using local grid refinement have been developed for use with these models. However, this strategy requires a very complex data structure, yields large numbers of contour points, and is inconsistent with our previously presented topology-preserving geometric deformable model (TGDM). In this paper, we incorporate an alternative adaptive grid technique called the moving grid method into the geometric deformable model framework. We find that it is simpler to implement than grid refinement, requiring no large, complex, hierarchical data structures. It also limits the number of contour vertices in the final contour and supports the incorporation of the topology-preserving constraint of TGDM. After presenting the algorithm, we demonstrate its performance using both simulated and real images.

title: Optimal Parameters for Load Balancing with the Diffusion Method in Mesh Networks
abstract: The diffusion method is a simple distributed load balancing method for distributed memory multiprocessors. It operates in a relaxation fashion for point-to-point networks. Its convergence to the balanced state relies on the value of a parameter—the diffusion parameter. An optimal diffusion parameter would lead to the fastest convergence of the method. Previous results on optimal parameters have existed for the k-ary n-cube and the torus. In this paper, we derive optimal diffusion parameters for mesh networks.

title: Design and evaluation of an environment APE for automatic parallelization of programs
abstract: In this paper, we present the design and evaluation of a compiler system, called APE, for automatic parallelization of scientific and engineering applications on distributed memory computers. APE is built on top of SUIF compiler. It extends SUIF with capabilities in parallelizing loops with non-uniform cross-iteration dependencies, and in handling loops that have indirect access patterns. We have evaluated the effectiveness of SUIF with several CFD test codes, and found that SUIF handles uniform loops over dense and regular data structures very well. For non-uniform loops, an innovative and efficient parallelization approach based on convex theory have been proposed and is being implemented. We have also presented a class of scalable algorithms for parallel distribution and redistribution of unstructured data structures during parallelizing irregular loops.

title: Stochastic Prediction of Execution Time for Dynamic Bulk Synchronous Computations
abstract: We consider the problem of execution time prediction for non-deterministic multi-phase bulk synchronous computations in multiprocessors. We characterize the computations in two stochastic workload evolution models: additive and multiplicative. The additive model reflects the commutations in which the workload changes between phases are independent of processes' present workload. The multiplicative model becomes relevant when the workload change in a process is proportional to its load base. We take advantage of their salient features and show that conventional approaches based on central limit theorem in statistics are viable to predict the execution time for long run computations. By an elegant coordination of results from order statistics and convergence rates in the central limit theorem, we derive tighter bounds on execution time of short run computations, under some mild assumptions on their workload change distributions. Accuracy of the predictions is analyzed rigorously and verified by simulations.

title: Processing Rate Allocation for Proportional Slowdown Differentiation on Internet Servers
abstract: A proportional differentiation model states that quality of service of different classes of Internet traffic should be kept proportional to their prespecified differentiation parameters, independent of the class loads. The model has been applied in the proportional queueing delay differentiation (FDD) in both network core and network edges. However, in the server side, an important and interesting performance metric is slowdown, the ratio of a request's queueing delay to its service time. Slowdown is important because it is desirable that a request's delay be proportional to its processing requirement. We investigate the problem of processing rate allocation for proportional slowdown differentiation (PSD) on Internet servers. Existing algorithms for FDD provisioning in the network side are not applicable to PSD provisioning in the server side because slowdown is not only dependent on a job's queueing delay but also on its service time, which varies significantly depending on the requested services. We first derive a closed form expression of the expected slowdown in an M/Gp/1 FCFS queue, which is an M/G/l FCFS queue with a typical heavy-tailed service time distribution (bounded Pareto distribution). PSD provisioning is realized by deploying a task server for handling each request class in a FCFS way. We then develop a strategy of processing rate allocation for the task servers for PSD provisioning. Simulation results have showed that the proposed rate allocation strategy can provide predictable and controllable PSD services on the servers.

title: A robust packet scheduling algorithm for proportional delay differentiation services
abstract: Proportional delay differentiation (PDD) model is an important approach to relative differentiated services provisioning on the Internet. It aims to maintain pre-specified packet queueing-delay ratios between different classes of traffic at each hop. Existing PDD packet scheduling algorithms are able to achieve the goal in long time-scales when the system is highly utilized. This paper presents a new PDD scheduling algorithm, called Little's average delay (LAD), based on a proof of Little's Law. It monitors the arrival rate of the packets in each traffic class and the cumulative delays of the packets and schedules the packet according to their transient queueing properties in order to achieve the desired class delay ratios in both short and long time-scales. Simulation results show that LAD is able to provide predictable and controllable services in various system conditions and that such services, whenever feasible, can be guaranteed, independent of the distributions of packet arrivals and sizes. In comparison with other PDD scheduling algorithms, LAD can provide the same level of service quality in long time-scales and more accurate and robust control over the delay ratio in short time-scales. In particular, LAD outperforms its main competitors significantly when the desired delay ratio is large.