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Full-Text Articles in Physical Sciences and Mathematics
Approximation Algorithms For Effective Team Formation, George Rabanca
Approximation Algorithms For Effective Team Formation, George Rabanca
Dissertations, Theses, and Capstone Projects
This dissertation investigates the problem of creating multiple disjoint teams of maximum efficacy from a fixed set of workers. We identify three parameters which directly correlate to the team effectiveness — team expertise, team cohesion and team size — and propose efficient algorithms for optimizing each in various settings. We show that under standard assumptions the problems we explore are not optimally solvable in polynomial time, and thus we focus on developing efficient algorithms with guaranteed worst case approximation bounds. First, we investigate maximizing team expertise in a setting where each worker has different expertise for each job and each …
A Combinatorial Framework For Multiple Rna Interaction Prediction, Syed Ali Ahmed
A Combinatorial Framework For Multiple Rna Interaction Prediction, Syed Ali Ahmed
Dissertations, Theses, and Capstone Projects
The interaction of two RNA molecules involves a complex interplay between folding and binding that warranted recent developments in RNA-RNA interaction algorithms. However, biological mechanisms in which more than two RNAs take part in an interaction also exist.
A typical algorithmic approach to such problems is to find the minimum energy structure. Often the computationally optimal solution does not represent the biologically correct structure of the interaction. In addition, different biological structures may be observed, depending on several factors. Furthermore, scoring techniques often miss critical details about dependencies within different parts of the structure, which typically leads to lower scores …
Morphogenesis And Growth Driven By Selection Of Dynamical Properties, Yuri Cantor
Morphogenesis And Growth Driven By Selection Of Dynamical Properties, Yuri Cantor
Dissertations, Theses, and Capstone Projects
Organisms are understood to be complex adaptive systems that evolved to thrive in hostile environments. Though widely studied, the phenomena of organism development and growth, and their relationship to organism dynamics is not well understood. Indeed, the large number of components, their interconnectivity, and complex system interactions all obscure our ability to see, describe, and understand the functioning of biological organisms.
Here we take a synthetic and computational approach to the problem, abstracting the organism as a cellular automaton. Such systems are discrete digital models of real-world environments, making them more accessible and easier to study then their physical world …
Solving Algorithmic Problems In Finitely Presented Groups Via Machine Learning, Jonathan Gryak
Solving Algorithmic Problems In Finitely Presented Groups Via Machine Learning, Jonathan Gryak
Dissertations, Theses, and Capstone Projects
Machine learning and pattern recognition techniques have been successfully applied to algorithmic problems in free groups. In this dissertation, we seek to extend these techniques to finitely presented non-free groups, in particular to polycyclic and metabelian groups that are of interest to non-commutative cryptography.
As a prototypical example, we utilize supervised learning methods to construct classifiers that can solve the conjugacy decision problem, i.e., determine whether or not a pair of elements from a specified group are conjugate. The accuracies of classifiers created using decision trees, random forests, and N-tuple neural network models are evaluated for several non-free groups. …