1,167 research outputs found
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
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
Klondike Elementary School: Serving Purdue and the Local Community and Empowering Students from around the Globe through High-Quality Learning Experiences Accenting Diversity and Inclusion
Disabilities Awareness Program (DAP) aims to cultivate an understanding of disability and inclusion among young children through accessible instructions, age-appropriate activities, and engaging discussions. We want to take this opportunity to introduce one of our school partners - Klondike Elementary School (KES), to recognize its contributions to DAP in providing service-learning opportunities and showcase KES as one of the community partners
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
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