As the world is flooded with data, the demand for mining data for useful purposes is increasing. An effective techniques is to model the data as networks (graphs) and then apply graph mining techniques for analysis. As on date, the algorithms available to count graphlets and orbits for various types of graphs and their generalizations are limited. The thesis aims to fill the gap by presenting a simple and efficient algorithm for 3-node graphlet and orbit counting that is generic enough to work for both undirected and directed graphs. Our algorithm is compared with the state-of-art algorithms and we show that in most cases our algorithm performs better. We demonstrate our algorithm in three case studies related to (i) enzyme and metabolite correlation network in corn, (ii) watershed governance networks, and (iii) patterns exhibited by co-expression networks of healthy and cancerous stomach cells.
This thesis presents and discusses a potential method for solving the dynamic obstacle avoidance problem using contemporary work with artificial neural networks (ANNs) and genetic algorithms (GAs) in combination with an imitation of a biological genetic process called segmental duplication. ANNs, GAs and segmental duplication are merged in the project to form SDNEAT, a type of evolutionary artificial neural network (EANN) system based on NeuroEvolution of Augmenting Topologies, or NEAT. The system is then used to develop an artificial neural network system that attempts to navigate environments incorporating both static and dynamic obstacles.
Pathfinding is one of the main problems for computer gaming. It has long been a bottleneck for system performance in the online game industry. Due to the vast amount of pathfinding requests and attributes of game maps, many pathfinding methods that work well for the console game environment have failed the challenges of online games. In order to obtain a satisfactory performance, the background processing system has to sacrifice either efficiency or accuracy otherwise it would require a hardware improvement. Therefore, after investigating possible solutions to resolve these common issues of pathfinding, we have designed a Heuristic Path Finding Method. Under this method, designers analyze the game map structure and build area information first. The online game system will then generate path templates for in-game usage based on the map information. As the templates are being generated, the system's pathfinding Artificial Intelligence (AI) will pick a path from the templates and adjust it accordingly to produce a real path. This method improves pathfinding tasks with higher accuracy, is less time consuming and requires fewer resources from the game system. We have also created a testing system as a tool for testing and evaluating pathfinding related work. We carried out a series of experiments with the testing system on the online game service, and showed us that our method is a better solution than a few known algorithms.--P. i.
The nature was the source of inspiration in many designs and products. Learning the algorithms from nature and incorporating them in the product design can be another level of inspiration. Algorithms for generating the pattern of tree-growth and venation in a leaf are nature-based algorithms for various uses in design and modeling. Development of the plant-pattern generators was initially intolerant of target shape until introduction of the space colonization algorithm (SCA). The SCA had a target area filled with points. The nodes, which create the final shape, start growing the pattern from an initial point to cover the target area. The points have an attraction field, which determines the direction of pattern. This project consists of two phases. The first phase improved some the features in the SCA including: i) the capability of starting the branching pattern from outside the target area, ii) tolerating symmetric distribution of points in target area, and iii) not canceling the effect of points from each other. The second phase used a branching equation assigned thickness to the members. The parameter in the equation was optimized to achieve the minimum variance of stress/capacity ratio among members.
Mycoheterotrophy is a nutritional strategy where plants obtain some portion of their required carbon through mycorrhizal fungi. This four-year study assessed ¹³C and ¹⁵N stable isotopes, gas-exchange data and population densities of putative partial (PMH) and full mycoheterotrophic (MH) ericaceous species relative to assorted autotrophs across various habitats and disturbance levels. Two of the putative PMH Pyroleae species exhibited approximately 30% MH carbon gains; however, some data indicated that ¹³C enrichment in Pyroleae species may result from unique autotrophic physiology or metabolism rather than fungal carbon acquisition. Regardless of nutritional status, the Pyroleae appeared sensitive to high irradiance. Long-term exposure to excess light may have contributed to photosynthetic impairment and population declines observed in clearcuts, where residual vegetation appeared to promote resilience by providing shade and nitrogen obtained through mycorrhizal fungi. This study provides valuable insight into physiological and environmental limitations of plant species that are partially to fully mycoheterotrophic.
Recent advancement of artificial intelligence (AI) techniques have impacted the field of algorithmic music composition, and that has been evidenced by live concert performances wherein the audience reportedly often could not tell whether music was composed by machine or by human. Among the various AI techniques, genetic algorithms dominate the field due to their suitability for both creativity and optimization. Many attempts have been made to incorporate rules from traditional music theory to design and automate genetic algorithms. Another popular approach is to incorporate statistical or mathematical measures of fitness. However, these rules and measures are rarely tested for their validity. This thesis is aimed at addressing the above limitation and hence paving the way to advance the field towards composing human-quality music. The basic idea is to look beyond this constrained set of traditional music rules and statistical/mathematical methods towards a more concrete foundation. We look to a field at the intersection of musicology and psychology, referred to as music-psychology. To demonstrate our proposed approach, we implemented a genetic algorithm exclusively using rules found in music-psychology. An online survey was conducted testing the quality of our algorithm’s output compositions. Moreover, algorithm performance was analyzed by experimental study. The initial results are encouraging and warrant further research. The societal implications of our work and other research in the field are also discussed.
