Discriminative Indexing for Probabilistic Image Patch Priors

Abstract. Newly emerged probabilistic image patch priors, such as Expected Patch Log-Likelihood (EPLL), have shown excellent performance on image restoration tasks, especially deconvolution, due to its rich expressiveness. However, its applicability is limited by the heavy computation involved in the associated optimization process. Inspired by the recent advances on using regression trees to index priors defined on a Conditional Random Field, we propose a novel discriminative indexing approach on patch-based priors to expedite the optimization process. Specifically, we propose an efficient tree indexing structure for EPLL, and overcome its training tractability challenges in high-dimensional spaces by utilizing special structures of the prior. Experimental results show that our approach accelerates state-of-the-art EPLL-based deconvolution methods by up to 40 times, with very little quality compromise.

EXPERIENCE-ORIENTED MODEL OF BUDGET ALLOCATION AND COST CONTROL FOR ENGINEERING CONSULTING PROJECTS

This paper presents an experience-oriented model of budget allocation and cost control for engineering consulting projects. The proposed model comprised two modules: a work item module and a work duration module. Regarding the work item module, a project manager employed the analytic hierarchy process (AHP) to determine the budget percentage allocated to each work item. Regarding the work duration module, this study compiled all S-curves appearing in each budget percentage range in past projects. A project manager then selected the optimal curve shape for each work item to determine the daily budget allocation and cost control limits throughout the work duration of each work item. Testing revealed that the proposed model facilitates project managers’ budget allocation decision-making, determines budget control limits for the overall project and for each work item and identifies work items that may be out of control at an early stage

New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms

In order to perform big-data analytics, regression involving large matrices is often necessary. In particular, large scale regression problems are encountered when one wishes to extract semantic patterns for knowledge discovery and data mining. When a large matrix can be processed in its factorized form, advantages arise in terms of computation, implementation, and data-compression. In this work, we propose two new parallel iterative algorithms as extensions of the Gauss–Seidel algorithm (GSA) to solve regression problems involving many variables. The convergence study in terms of error-bounds of the proposed iterative algorithms is also performed, and the required computation resources, namely time-and memory-complexities, are evaluated to benchmark the efficiency of the proposed new algorithms. Finally, the numerical results from both Monte Carlo simulations and real-world datasets are presented to demonstrate the striking effectiveness of our proposed new methods