Skip to Main Content

Update

Your source for campus news

Attend a Graduate Defense

By: taylorreeves737   Published 2:50 pm / February 27, 2017

The campus community is invited to attend the following graduate defenses:

Spencer Nelson

When: 1 p.m. Tuesday, May 9
Where: Math Building, Room 139
Title: The Random Graph and Reciprocity Laws
Program: Master of Science in Mathematics

Abstract:
The theory of random graphs, that is graphs generated by some prescribed random process, gained popularity in the late 1950s and the level of interest has only increased since then. Random graphs on a countably infinite set of vertices is the subject of this thesis. We show that almost all graphs on countably many vertices are isomorphic to each other, implying that there is only one random graph, namely the random graph, on countably many vertices (up to isomorphism). We will survey some historical results concerning the random graph, present a number of its graph theoretic properties, as well as explicit examples based on familiar concepts.

Akshay Kansal

When: 2 p.m. Thursday, May 11
Where: City Center Plaza, Room 352
Title: A Scalable Graph-Coarsening based Index for Dynamic Graph Databases
Program: Master of Science in Computer Science

Abstract: 
Graph is a commonly used data structure for modeling complex data such as chemical molecules, images, social networks, and XML documents. This complex data is stored using a set of graphs, known as graph database D. To speed up query answering on graph databases, indexes are commonly used. State-of- the-art graph database indexes such as gIndex and FG-Index do not adapt or scale well to dynamic graph database use; they are static, and their ability to prune possible search responses to meet user needs worsens over time as databases change and grow. Users can re-mine indexes to gain some improvement, but it is time consuming. Users must also tune numerous parameters on an ongoing basis to optimize performance and can inadvertently worsen the query response time if they do not choose parameters wisely.

Recently, a one-pass algorithm has been developed to enhance the performance of these indexes in part by using the algorithm to update them regularly. However, there are some drawbacks, most notably the need to make updates as the query workload changes.We propose a new index based on graph-coarsening to speed up query answering time in dynamic graph databases. Our index is parameter-free, query-independent, scalable, small enough to store in the main memory, and is simpler and less costly to maintain for database updates. Experimental results show that our index outper-forms gIndex and FG-Index for query answering time in the case of social network databases, and is comparable with these indexes for frequent and infrequent queries on chemical databases. Our graph-coarsening index can be updated up to 60 times faster in comparison to one-pass on dynamic graph databases. Moreover, our index is independent of the query workload for index update and is up to 15 times better after hybrid indexes are attuned to query workload.

Matthew Swenson

When: 1 p.m. Wednesday, May 17
Where: Micron Engineering Center, Room 106
Title: The Mechanism of Radiation-Induced Nanocluster Evolution in Oxide Dispersion Strengthened and Ferritic-Martensitic Alloys
Program: Doctor of Philosophy in Materials Science and Engineering

Abstract: 
The objective of this study is to evaluate the mechanism of irradiation-induced nanoparticle evolution in a model Fe-9%Cr oxide dispersion strengthened steel and commercial ferritic-martensitic alloys HCM12A and HT9. Each alloy is irradiated with Fe2+ ions, protons, or neutrons to doses ranging from 1-100 displacements per atoms at 500°C. The morphology of nanoclusters are characterized using atom probe tomography. The evolution of clusters in each alloy are notably different with each irradiating particle, and the competing effects of ballistic dissolution and radiation-enhanced, diffusion-driven growth are attributed to the respective differences in cluster evolution. A phase evolution model, originally theorized by Nelson, Hudson, and Mazey, is used to simulate time-dependent nanocluster irradiation evolution in each alloy, with useful insights achieved to inform future alloy development. In all cases, a downward temperature shift is required to emulate low-dose- rate nanocluster evolution using higher-dose- rate irradiations.