High Performance Issues in Image Processing and Computer Vision

Typical image processing and computer vision tasks found in industrial, medical, and military applications require real-time solutions. These requirements have motivated the design of many parallel architectures and algorithms. Recently, a new architecture called the reconfigurable mesh has been proposed. This thesis addresses a number of problems in image processing and computer vision on reconfigurable meshes. We first show that a number of low-level descriptors of a digitized image such as the perimeter, area, histogram and median row can be reduced to computing the sum of all the integers in a matrix, which in turn can be reduced to computing the prefix sums of a binary sequence and the prefix sums of an integer sequence. We then propose a new computational paradigm for reconfigurable meshes, that is, identifying an entity by a bus and performing computations on the bus to obtain properties of the entity. Using the new paradigm, we solve a number of mid-level vision tasks including the Hough transform and component labeling. Finally, a VLSI-optimal constant time algorithm for computing the convex hull of a set of planar points is presented based on a VLSI-optimal constant time sorting algorithm. As by-products, two basic data movement techniques, computing the prefix sums of a binary sequence and computing the prefix maxima of a sequence of real numbers, and a VLSI-optimal constant time sorting algorithm have been developed. These by-products are interesting in their own right. In addition, they can be exploited to obtain efficient algorithms for a number of computational problems

Inflammation-resolving lipid mediators promote revascularization to enhance wound healing.

Wound healing is a highly concerted cellular process that begins with inflammation and proceeds to resolution to revascularize the site of injury. Although inflammation is essential to revascularization during wound healing, it is now recognized that resolution is an active process that is equally important. Other investigations have implicated a beneficial effect of resolving inflammation and promoting resolution in the remission of inflammatory pathologies. Recently investigations have yielded a novel class of ?-3 fatty acid derived lipid mediators, biosynthesized by leukocytes, which are capable of resolving inflammation and promoting resolution. We therefore hypothesized that these leukocyte-derived pro-resolving lipid mediators can promote revascularization to enhance wound healing. In the following studies, we provide evidence supporting this hypothesis: 1) that the ?-3 fatty acid derived pro-resolution lipid mediator Resolvin D2 enhances perfusion recovery of ischemic tissue 2) that inflammatory monocytes, which increase during revascularization, synthesize Resolvin D2 through the 12/15-lipoxygenase pathway 3) and that resolvin D2 does not stimulate angiogenesis, but stimulates arteriogenesis through the promotion of endothelial cell migration. These results suggest that Resolvin D2 can enhance revascularization and may be an effective therapeutic agent

Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM) 201

Discovering Organizational Correlations from Twitter

Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because important information is usually not publicly available (e.g., the correlations of terrorist organizations). Nowadays, an increasing amount of organizational information can be posted online by individuals and spread instantly through Twitter. Such information can be crucial for detecting organizational correlations. In this paper, we study the problem of discovering correlations among organizations from Twitter. Mining organizational correlations is a very challenging task due to the following reasons: a) Data in Twitter occurs as large volumes of mixed information. The most relevant information about organizations is often buried. Thus, the organizational correlations can be scattered in multiple places, represented by different forms; b) Making use of information from Twitter collectively and judiciously is difficult because of the multiple representations of organizational correlations that are extracted. In order to address these issues, we propose multi-CG (multiple Correlation Graphs based model), an unsupervised framework that can learn a consensus of correlations among organizations based on multiple representations extracted from Twitter, which is more accurate and robust than correlations based on a single representation. Empirical study shows that the consensus graph extracted from Twitter can capture the organizational correlations effectively.Comment: 11 pages, 4 figure

Mathematics RTI/MTSS Implementation: A Literature Review from the Perspective of Implementation Science

This article reviews published research on implementing the Response to Intervention (RTI)/Multi-tiered System of Support (MTSS) educational framework in mathematics at schools. We utilized the Implementation Driver framework from Implementation Science (Eccles & Mittman, 2006) to analyze current RTI/MTSS implementation practices. Eleven studies qualified to be included in this research. Findings showed more research is needed to expand the investigations in implementation fidelity, systems intervention, facilitative administration, decision-support data systems, coaching, and selection driver