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https://viurrspace.ca/bitstream/handle/10613/16537/Feb16-1901.pdf?sequence=2

Much research has been done to examine the relation between investors' human capital and their financial asset allocation. While some showed that the value of human capital should be taken into consideration to make financial asset allocation decisions on the composition of investing portfolios, most argued not. In this paper, we selected the monthly return of 9 industrial ETFs from June of 2007 to July 2011, used the present value of total future income as estimate of human capital, and relied on the Mean-Variance Optimal Asset Allocation framework to reexamine if human capital will impact investors optimal financial portfolios. Based on our tests, we found significant connection between human capital and risky asset allocation, which resulted in significant change to weights allocated to the risk assets to create a Mean-Variance optimal portfolio.

The goal is to prove a surprising lower bound for resource augmented nonclairvoyant algorithms for scheduling jobs with sublinear nondecreasing speed-up curves on multiple processors with the objective of average response time. Edmonds and Pruhs in SODA09 prove that for every $e > 0$, there is an algorithm $alg_{e}$ that is $(1!+!epsilon)$-speed $O({1 over e2})$-competitive. A problem, however, is that this algorithm $alg_{e}$ depends on $e$. The goal is to prove that every fixed deterministic nonclairvoyant algorithm has a suboptimal speed threshold, namely for every (graceful) algorithm $alg$, there is a threshold $1!+!beta_{alg}$ that is $beta_{alg} > 0$ away from being optimal such that the algorithm is $Omega({1 over e beta_{alg}})$ competitive with speed $(1 !+! beta_{alg}) !+! e$ and is $omega(1)$ competitive with speed $1 !+! beta_{alg}$. I have worked very hard on it and have felt that I was close. The proof technique is to use Brouwer's fixed point theorem to break the cycle of needing to know which input will be given before one can know what the algorithm will do and needing to know what the algorithm will do before one can know which input to give. Every thing I have can be found at

https://viurrspace.ca/bitstream/handle/10613/21807/Jan18-1893.pdf?sequence=2

We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning. This model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized algorithms to evaluate alternatives and produce votes (captured by the second-stage noise models). We analytically demonstrate that a uniform team, consisting of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents grows. Our experiments, which pit teams of computer Go agents against strong agents, provide evidence for the effectiveness of voting when agents are diverse.

https://viurrspace.ca/bitstream/handle/10613/15057/Oct18-1917.pdf?sequence=2&isAllowed=y

https://viurrspace.ca/bitstream/handle/10613/19025/Dec26-1874.pdf?sequence=2&isAllowed=y

https://viurrspace.ca/bitstream/handle/10613/17324/Nov16-1898.pdf?sequence=2