Recent Submissions

  • Demonstration of deterministic diffusion in two dimensions

    Fukś, Henryk (2023-12-18)
    Video recording (animation) of the time evolution of deterministic cellular automaton emulating diffusion in 2D. Detailed description of the cellular automaton in: H. Fukś, Four state deterministic cellular automaton rule emulating random diffusion. In B. Chopard, editor, Cellular Automata, ACRI 2022, LNCS 13402, pages 142--152. Springer, 2022, http://dx.doi.org/10.1007/978-3-031-14926-9_13 .
  • Deterministic cellular automaton rule emulating 2D diffusion

    Fukś, Henryk (2023-12-18)
    Files defining 8-state cellular automaton emulating diffusion in 2 dimensions. Rule definition is in golly's .rule format. Sample initial pattern is included. Details of the rule are described in: H. Fukś. Four state deterministic cellular automaton rule emulating random diffusion. In B. Chopard, editor, Cellular Automata, ACRI 2022, LNCS 13402, pages 142--152. Springer, 2022, http://dx.doi.org/10.1007/978-3-031-14926-9_13 .
  • Demonstration of density classification by two 2D probabilistic cellular automata

    Fukś, Henryk (2023-12-15)
    Demonstration of the solution of the density classification problem for initial density 0.501, performed by a pair of 2D cellular automaton rules described in H. Fukś, "Solving two-dimensional density classification problem with two probabilistic cellular automata", Journal of Cellular Automata, 10(1--2):149--160, 2015 (also availabe at https://arxiv.org/abs/1506.06653). The rules used are generalized ECA 184 with random "lane changes" and generalized ECA 232 with random "crowd avoidance".
  • Program constructing lunar tables for ecclesiastical moon

    Fukś, Henryk (2023-12-15)
    Python program producing lunar tables similar to those found in Martyrologium Romanum. Its main purpose is to determine the age of the eccesiastical moon on a given calendar day, using algorithm given in Martylorogium Romanum and implemented as described in H. Fukś, Antiquitates Mathematicae, Vol 16 (2022) , 259-282.
  • Bearded dragons (Pogona vitticeps) with reduced scalation lose water faster but do not have substantially different thermal preferences.

    Sakich, Nicholas B; Tattersall, Glenn J (2021-06-17)
    Whether scales reduce cutaneous evaporative water loss in lepidosaur reptiles (Superorder Lepidosauria) such as lizards and snakes has been a contentious issue for nearly half a century. Furthermore, while many studies have looked at whether dehydration affects thermal preference in lepidosaurs, far fewer have examined whether normally hydrated lepidosaurs can assess their instantaneous rate of evaporative water loss and adjust their thermal preference to compensate in an adaptive manner. We tested both of these hypotheses using three captive-bred phenotypes of bearded dragon (Pogona vitticeps) sourced from the pet trade: ‘Wild Types’ with normal scalation, ‘Leatherbacks’ exhibiting scales of reduced prominence, and scaleless bearded dragons referred to as ‘Silkbacks’. Silkbacks on average lost water evaporatively at about twice the rate that Wild Types did. Leatherbacks on average were closer in their rates of evaporative water loss to Silkbacks than they were to Wild Types. Additionally, very small (at most ~1°C) differences in thermal preference existed between the three phenotypes that were not statistically significant. This suggests a lack of plasticity in thermal preference in response to an increase in rate of evaporative water loss, and may be reflective of a thermal ‘strategy’ as employed by thermoregulating bearded dragons that prioritises immediate thermal benefits over the threat of future dehydration. The results of this study bolster an often-discounted hypothesis regarding the present adaptive function of scales and have implications for the applied fields of animal welfare and conservation.
  • Faculty of Mathematics and Science 1st Graduate Research Day Conference, 2022

    2023-01-06
    FMS Graduate Research Day (FMS GRaD) is an academic conference open to all FMS students with a mandate to celebrate and communicate Brock University research and teaching. The FMS GRaD 2022 conference was hosted by the Dean’s office of the Faculty of Mathematics and Science and Graduate Mathematics and Science Society at Brock University. With 57 presenters and over 300 attendees this first FMS GRaD held on September 16th 2022 strengthened the STEM research community and highlight the research and profile of FMS graduate student research programs.
  • Parameter selection for modeling of epidemic networks

    Dube, Michael; Houghten, Sheridan; Ashlock, Daniel (IEEE, 2018-5)
    The accurate modeling of epidemics on social contact networks is difficult due to the variation between different epidemics and the large number of parameters inherent to the problem. To reduce complexity, evolutionary computation is used to create a generative representation of the epidemic model. Previous gains from the use of local, verses global, operators are further explored to better balance exploration and exploitation of the genetic algorithm. A typical parameter study is conducted to test this new local operator and the new method of point packing is utilized as a proof of concept to perform a better search of the parameter space. All experiments from both approaches are tested against nine epidemic profiles. The point-packing driven parameter search demonstrates that the algorithm parameters interact substantially and in a non-linear fashion, and also shows that the good parameter settings are problem specific.
  • Edit metric decoding: Return of the side effect machines

    Houghten, Sheridan; Collins, Tyler K.; Hughes, James Alexander; Brown, Joseph Alexander (IEEE, 2018-5)
    Side Effect Machines (SEMs) are an extension of finite state machines which place a counter on each node that is incremented when that node is visited. Previous studies examined a genetic algorithm to discover node connections in SEMs for edit metric decoding for biological applications, namely to handle sequencing errors. Edit metric codes, while useful for decoding such biologically created errors, have a structure which significantly differentiates them from other codes based on Hamming distance. Further, the inclusion of biologically- motivated restrictions on allowed words makes development of decoders a bespoke process based on the exact code used. This study examines the use of evolutionary programming for the creation of such decoders, thus allowing for the number of states to be evolved directly, not witnessed in previous approaches which used genetic algorithms. Both direct and fuzzy decoding are used, obtaining correct decoding rates of up to 95% in some SEMs.
  • A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data

    Kazemi, Yosra; Houghten, Sheridan (IEEE, 2018-5)
    Alzheimer's disease (AD) is an irreversible, progressive neurological disorder that causes memory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from neuroimaging data in order to distinguish different stages of Alzheimer's disease (AD). However, the similarity of the brain patterns in older adults and in different stages makes the classification of different stages a challenge for researchers. In this paper, convolutional neuronal network architecture AlexNet was applied to fMRI datasets to classify different stages of the disease. We classified five different stages of Alzheimer's using a deep learning algorithm. The method successfully classified normal healthy control (NC), significant memory concern (SMC), early mild cognitive impair (EMCI), late cognitive mild impair (LMCI), and Alzheimer's disease (AD). The model was implemented using GPU high performance computing. Before applying any classification, the fMRI data were strictly preprocessed. Then, low to high level features were extracted and learned using the AlexNet model. Our experiments show significant improvement in classification. The average accuracy of the model was 97.63%. We then tested our model on test datasets to evaluate the accuracy of the model per class, obtaining an accuracy of 94.97% for AD, 95.64% for EMCI, 95.89% for LMCI, 98.34% for NC, and 94.55% for SMC.
  • Hierarchical clustering and tree stability

    Saunders, Amanda; Ashlock, Daniel; Houghten, Sheridan (IEEE, 2018-5)
    Hierarchical clustering via neighbor joining, widely used in biology, can be quite sensitive to the addition or deletion of single taxa. In an earlier study it was found that neighbor joining trees on random data were commonly quite unstable in the sense that large re-arrangements of the tree occurred when the tree was reconstructed after the deletion of a single data point. In this study, we use an evolutionary algorithm to evolve extremely stable and unstable data sets for a standard neighbor-joining algorithm and then check the stability using a novel type of clustering called bubble clustering. Bubble clustering is an instance of associator clustering. The stability measure used is based on the size of the subtree containing each pair of taxa, a quantity that provides an objective measure of a given trees hypothesis about the relatedness of taxa. It is shown experimentally that even in data sets evolved to be stable for a standard neighbor joining algorithm, bubble clustering is a significantly more stable algorithm.
  • Representation for Evolution of Epidemic Models

    Dube, Michael; Houghten, Sheridan; Ashlock, Daniel (IEEE, 2019-6)
    Creating a representation capable of generating personal contact networks that are most likely to exhibit specific epidemic behavior is difficult due to the inherit volatility of an epidemic and the numerous parameters accompanying the problem. To surpass these hurdles, evolutionary algorithms are used to create a generative solution which generates personal contact networks, modeling human populations, to satisfy the epidemic duration and epidemic profile matching problems. This representation is entitled the Local THADS-N representation. Two new operators are added to the original THADS-N system, and tested with a traditional parameter sweep and a parameter selection method known as point packing on nine epidemic profiles. Additionally, a new epidemic model is implemented in order to allow for lost immunity within a population thus increasing the length of an epidemic.
  • We Are Not Pontius Pilate: Acknowledging Ethics and Policy

    Hughes, James Alexander; Hannah, William; Kikkert, Peter; MacKenzie, Barry; Ashlock, Wendy; Houghten, Sheridan; Ashlock, Daniel; Stoodley, Matthew; Dube, Michael; Brown, Rachel; et al. (IEEE, 2020-12-01)
    A new AI system is being developed to optimize vaccination strategies based on the structure and shape of a community's social contact network. The technology is minimally constrained and not bound by preconceived notions or human biases. With this come novel outside the box strategies; however, the system is only capable of optimizing what it is instructed to optimize, and does not consider any ethical or political concerns. With the growing concern for systematic discrimination as a result of artificial intelligence, we acknowledge a number of relevant issues that may arise as a consequence of our new technology and categorize them into three classes. We also introduce four normative ethical approaches that are used as a framework for decision-making. Despite the focus on vaccination strategies, our goal is to improve the discussions surrounding public concern and trust over artificial intelligence and demonstrate that artificial intelligence practitioners are addressing these concerns.
  • Culture of Cancer Cells at Physiological Oxygen Levels Affects Gene Expression in a Cell-Type Specific Manner

    Alva, Ricardo; Moradi, Fereshteh; Liang, Ping; Stuart, Jeffrey A. (MDPI, 2022)
    Standard cell culture is routinely performed at supraphysiological oxygen levels (~18% O2). Conversely, O2 levels in most mammalian tissues range from 1–6% (physioxia). Such hyperoxic conditions in cell culture can alter reactive oxygen species (ROS) production, metabolism, mitochondrial networks, and response to drugs and hormones. The aim of this study was to investigate the transcriptional response to different O2 levels and determine whether it is similar across cell lines, or cell line-specific. Using RNA-seq, we performed differential gene expression and functional enrichment analyses in four human cancer cell lines, LNCaP, Huh-7, PC-3, and SH-SY5Y cultured at either 5% or 18% O2 for 14 days. We found that O2 levels affected transcript abundance of thousands of genes, with the affected genes having little overlap between cell lines. Functional enrichment analysis also revealed different processes and pathways being affected by O2 in each cell line. Interestingly, most of the top differentially expressed genes are involved in cancer biology, which highlights the importance of O2 levels in cancer cell research. Further, we observed several hypoxia-inducible factor (HIF) targets, HIF-2α targets particularly, upregulated at 5% O2, consistent with a role for HIFs in physioxia. O2 levels also differentially induced the transcription of mitochondria-encoded genes in most cell lines. Finally, by comparing our transcriptomic data from LNCaP and PC-3 with datasets from the Prostate Cancer Transcriptome Atlas, a correlation between genes upregulated at 5% O2 in LNCaP cells and the in vivo prostate cancer transcriptome was found. We conclude that the transcriptional response to O2 over the range from 5–18% is robust and highly cell-type specific. This latter finding indicates that the effects of O2 levels are difficult to predict and thus highlights the importance of regulating O2 in cell culture.
  • Data to accompany manuscript: Thermoconforming rays of the star-nosed mole

    Tattersall, Glenn; Campbell, Kevin (2022-11-18)
    The star-nosed mole (Condylura cristata) is well known for its unique star-like rostrum (‘star’) which is formed by 22 nasal appendages highly specialised for tactile sensation. As a northerly distributed insectivorous mammal occupying both aquatic and terrestrial habitats, this sensory appendage is regularly exposed to cold water and thermally conductive soil, leading us to ask whether the surface temperature, a proxy for blood flow to the star, conforms to the local ambient temperature to conserve body heat. Alternatively, given the high functioning and sensory nature of the star, we posited it was possible that the rays may be kept continually warm when foraging, with augmented peripheral blood flow serving metabolic needs of this tactile sensory organ. To test these ideas, we remotely monitored the surface temperatures of the star and other uninsulated appendages in response to changes in local water or ground temperature in wild-caught star-nosed moles briefly studied in captive situation. While the tail responded to increasing heat load through vasodilation, the surface temperature of the star consistently thermoconformed, varying passively in surface temperature, suggesting little evidence for thermoregulatory vasomotion. This thermoconforming response may have evolved as a compensatory response related to the high costs of heat dissipation to water or soil in this actively foraging insectivore.
  • Pandemic: A Graph Evolution Story

    Dube, Michael; Houghten, Sheridan; Ashlock, Daniel (IEEE, 2019-07)
    The Graph Evolution Tool (GET)was built to generate personal contact networks representing who can infect whom within a community. The tool is expanded in order to permit an infection scheme which divides the community into different districts, thus permitting within-district and between-district infections. The evolutionary algorithm comprising GET is expanded upon to simulate communities which include 512 individuals in up to eight districts, initially infecting one person in one district and spreading through a community. The overall goal is to generate communities that will maximize the length of an epidemic. The problem associated with adequately exploring the numerous parameters accompanying evolutionary algorithms is addressed using a point packing and insight from previous work. The Susceptible-Infected-Removed (SIR)model of infection was chosen as it provides a sufficient balance of simplicity and complexity for the problem.
  • Using Genetic Programming to Investigate a Novel Model of Resting Energy Expenditure for Bariatric Surgery Patients

    Hughes, James Alexander; Reid, Ryan E. R.; Houghten, Sheridan; Andersen, Ross E. (IEEE, 2020-10-27)
    Traditionally, models developed to estimate resting energy expenditure (REE) in the bariatric population have been limited to linear modelling based on data from `normal' or `overweight' individuals - not `obese'. This type of modelling can be restrictive and yield functions which poorly estimate this important physiological outcome.Linear and nonlinear models of REE for individuals after bariatric surgery are developed with linear regression and symbolic regression via genetic programming. Features not traditionally used in REE modelling were also incorporated and analyzed and genetic programming's intrinsic feature selection was used as a measure of feature importance.A collection of effective new linear and nonlinear models were generated. The linear models generated outperformed the nonlinear on testing data, although the nonlinear models fit the training data better. Ultimately, the newly developed linear models showed an improvement over existing models and the feature importance analysis suggested that the typically used features (age, weight, and height) were the most important.
  • Deep Learning for the Prediction of Stock Market Trends

    Fazeli, Arvand; Houghten, Sheridan (IEEE, 2019-12)
    In this study, deep learning will be used to test the predictability of stock trends. Stock markets are known to be volatile, prices fluctuate, and there are many complicated financial indicators involved. Various data including news or financial indicators can be used to predict stock prices. In this study, the focus will be on using past stock prices and using technical indicators to increase the performance of the results. The goal of this study is to measure the accuracy of predictions and evaluate the results. Historical data is gathered for Apple, Microsoft, Google and Intel stocks. A prediction model is created by using past data and technical indicators were used as features in the model. The experiments were performed by using long short-term memory networks. Different approaches and techniques were tested to boost the performance of the results. To prove the usability of the final model in the real world and measure the profitability of results backtesting was performed. The final results show that while it is not possible to predict the exact price of a stock in the future to gain profitable results, deep learning can be used to predict the trend of stock markets to generate buy and sell signals.
  • Compression of Biological Networks using a Genetic Algorithm with Localized Merge

    Houghten, Sheridan; Romualdo, Angelo; Collins, Tyler K.; Brown, Joseph Alexander (IEEE, 2019-07)
    Network graphs appear in a number of important biological data problems, recording information relating to protein-protein interactions, gene regulation, transcription regulation and much more. These graphs are of such a significant size that they are impossible for a human to understand. Furthermore, the ever-expanding quantity of such information means that there are storage issues. To help address these issues, it is common for applications to compress nodes to form supernodes of similarly connected components. In previous graph compression studies it was noted that such supernodes often contain points from disparate parts of the graph. This study aims to correct this flaw by only allowing merges to occur within a local neighbourhood rather than across the entire graph. This restriction was found to not only produce more meaningful compressions, but also to reduce the overall distortion created by the compression for two out of three biological networks studied.

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