2022 GREAT Day Program

SUNY Geneseo’s Sixteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1016/thumbnail.jp

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Statistical Analysis and Stochastic Modelling of Foraging Bumblebees.

PhDIn the analysis of movement patterns of animals, stochastic processes play an important role, providing us with a variety of tools to examine, model and simulate their behaviour. In this thesis we focus on the foraging of specific animals - bumblebees - and analyse experimental data to understand the influence of changes in the bumblebees’ environment on their search flights. Starting with a discussion of main classes of stochastic models useful for the description of foraging animals, we then look at a multitude of environmental factors influencing the dynamics of animals in their search for food. With this background we examine flight data of foraging bumblebees obtained from a laboratory experiment by stochastic analyses. The main point of interest of this analysis is the description, modelling and understanding of the data with respect to the influence of predatory threats on the bumblebee’s foraging search flights. After this detail-oriented view on interactions of bumblebees with food sources and predators in the experimental data, we develop a generalized reorientation model. By extracting the necessary information from the data, we arrive at a generalized correlated random walk foraging model for bumblebee flights, which we discuss and compare to the experimental data via simulations. We finish with a discussion of anomalous fluctuation relations and some results on spectral densities of autocorrelation functions. While this part is not directly related to the analysis of foraging, it concerns a closely related class of stochastic processes described by Langevin equations with non- trivial autocorrelation functions analyse experimental data to understand the influence of changes in the bumblebees’ environment on their search flights. Starting with a discussion of main classes of stochastic models useful for the description of foraging animals, we then look at a multitude of environmental factors influencing the dynamics of animals in their search for food. With this background we examine flight data of foraging bumblebees obtained from a laboratory experiment by stochastic analyses. The main point of interest of this analysis is the description, modelling and understanding of the data with respect to the influence of predatory threats on the bumblebee’s foraging search flights. After this detail-oriented view on interactions of bumblebees with food sources and predators in the experimental data, we develop a generalized reorientation model. By extracting the necessary information from the data, we arrive at a generalized correlated random walk foraging model for bumblebee flights, which we discuss and compare to the experimental data via simulations. We finish with a discussion of anomalous fluctuation relations and some results on spectral densities of autocorrelation functions. While this part is not directly related to the analysis of foraging, it concerns a closely related class of stochastic processes described by Langevin equations with nontrivial autocorrelation functions

Angle modulated population based algorithms to solve binary problems

Recently, continuous-valued optimization problems have received a great amount of focus, resulting in optimization algorithms which are very efficient within the continuous-valued space. Many optimization problems are, however, defined within the binary-valued problem space. These continuous-valued optimization algorithms can not operate directly on a binary-valued problem representation, without algorithm adaptations because the mathematics used within these algorithms generally fails within a binary problem space. Unfortunately, such adaptations may alter the behavior of the algorithm, potentially degrading the performance of the original continuous-valued optimization algorithm. Additionally, binary representations present complications with respect to increasing problem dimensionality, interdependencies between dimensions, and a loss of precision. This research investigates the possibility of applying continuous-valued optimization algorithms to solve binary-valued problems, without requiring algorithm adaptation. This is achieved through the application of a mapping technique, known as angle modulation. Angle modulation effectively addresses most of the problems associated with the use of a binary representation by abstracting a binary problem into a four-dimensional continuous-valued space, from which a binary solution is then obtained. The abstraction is obtained as a bit-generating function produced by a continuous-valued algorithm. A binary solution is then obtained by sampling the bit-generating function. This thesis proposes a number of population-based angle-modulated continuous-valued algorithms to solve binary-valued problems. These algorithms are then compared to binary algorithm counterparts, using a suite of benchmark functions. Empirical analysis will show that the angle-modulated continuous-valued algorithms are viable alternatives to binary optimization algorithms. Copyright 2012, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Pamparà, G 2012, Angle modulated population based algorithms to solve binary problems, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd C12/4/188/gmDissertation (MSc)--University of Pretoria, 2012.Computer Scienceunrestricte

On Honey Bee Colony Dynamics and Disease Transmission

The work herein falls under the umbrella of mathematical modeling of disease transmission. The majority of this document focuses on the extent to which infection undermines the strength of a honey bee colony. These studies extend from simple mass-action ordinary differential equations models, to continuous age-structured partial differential equation models and finally a detailed agent-based model which accounts for vector transmission of infection between bees as well as a host of other influences and stressors on honey bee colony dynamics. These models offer a series of predictions relevant to the fate of honey bee colonies in the presence of disease and the nonlinear effects of disease, seasonality and the complicated dynamics of honey bee colonies. We are also able to extract from these models metrics that preempt colony failure. The analysis of disease dynamics in age-structured honey bee colony models required the study of next generation operators (NGO) and the basic reproduction number, $R_0$, for partial differential equations. This led us to the development of a coherent path from the NGO to its discrete compartmental counterpart, the next generation matrix (NGM) as well as the derivation of new closed-form formulae for the NGO for specific classes of disease models