Now showing items 1-18 of 18

  • Acoustic sequences in non-human animals : a tutorial review and prospectus 

    Kershenbaum, Arik; Blumstein, Dan; Roch, Marie; Akçay, Çaglar; Backus, Gregory; Bee, Mark A.; Bohn, Kirsten; Cao, Yan; Carter, Gerald; Cäsar, Cristiane; Coen, Michael; De Ruiter, Stacy Lynn; Doyle, Laurance; Edelman, Shimon; Ferrer-i-Cancho, Ramon; Freeberg, Todd M.; Garland, Ellen Clare; Gustison, Morgan; Harley, Heidi E.; Huetz, Chloé; Hughes, Melissa; Bruno, Julia Hyland; Ilany, Amiyaal; Jin, Dezhe Z.; Johnson, Michael; Ju, Chenghui; Karnowski, Jeremy; Lohr, Bernard; Manser, Marta; McCowan, Brenda; Mercado III, Eduardo; Narins, Peter M.; Piel, Alex; Rice, Megan; Salmi, Roberta; Sasahara, Kazutoshi; Sayigh, Laela; Shiu, Yu; Taylor, Charles; Vallejo, Edgar E.; Waller, Sara; Zamora-Gutierrez, Veronica (2016-02) - Journal article
    Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians,and mammals ...
  • An approach to situation recognition based on learned semantic models 

    Stevenson, Graeme (University of St Andrews, 2015-06-24) - Thesis
    A key enabler of pervasive computing is the ability to drive service delivery through the analysis of situations: Semantically meaningful classifications of system state, identified through analysing the readings from ...
  • Autonomous fault detection in self-healing systems : comparing Hidden Markov Models and artificial neural networks 

    Schneider, Christopher; Barker, Adam David; Dobson, Simon Andrew (ACM Press - Association for Computing Machinery, 2014-01-22) - Conference item
    Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating ...
  • Autonomous fault detection in self-healing systems using Restricted Boltzmann Machines 

    Schneider, Christopher; Barker, Adam David; Dobson, Simon Andrew (2014-09-24) - Conference item
    Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating ...
  • Evaluating unsupervised fault detection in self-healing systems using stochastic primitives 

    Schneider, Christopher; Barker, Adam David; Dobson, Simon Andrew (2015-01-28) - Journal article
    Autonomous fault detection represents one approach for reducing operational costs in large-scale computing environments. However, little empirical evidence exists regarding the implementation or comparison of such ...
  • Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification 

    Ballester, Pedro; Mangold, Martina; Howard, Nigel; Marchese Robinson, Richard; Abell, Chris; Blumberger, Jochen; Mitchell, John B. O. (2012-12-07) - Journal article
    One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into ...
  • Improving the efficiency of learning CSP solvers 

    Moore, Neil C.A. (University of St Andrews, 2011-05-01) - Thesis
    Backtracking CSP solvers provide a powerful framework for search and reasoning. The aim of constraint learning is increase global reasoning power by learning new constraints to boost reasoning and hopefully reduce search ...
  • Machine learning for systems pathology 

    Verleyen, Wim (University of St Andrews, 2013) - Thesis
    Systems pathology attempts to introduce more holistic approaches towards pathology and attempts to integrate clinicopathological information with “-omics” technology. This doctorate researches two examples of a systems ...
  • Machine learning in systems biology at different scales : from molecular biology to ecology 

    Aderhold, Andrej (University of St Andrews, 2015) - Thesis
    Machine learning has been a source for continuous methodological advances in the field of computational learning from data. Systems biology has profited in various ways from machine learning techniques but in particular ...
  • Machine learning methods in chemoinformatics 

    Mitchell, J.B.O. (2014-02-24) - Journal article
    Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular ...
  • On algorithm selection, with an application to combinatorial search problems 

    Kotthoff, Lars (University of St Andrews, 2012-06-20) - Thesis
    The Algorithm Selection Problem is to select the most appropriate way for solving a problem given a choice of different ways. Some of the most prominent and successful applications come from Artificial Intelligence and in ...
  • Predicting melting points of organic molecules : applications to aqueous solubility prediction using the General Solubility Equation 

    McDonagh, James; van Mourik, Tanja; Mitchell, John B. O. (2015-11) - Journal article
    In this work we make predictions of several important molecular properties of academic and industrial importance to seek answers to two questions: 1) Can we apply efficient machine learning techniques, using inexpensive ...
  • Predicting the protein targets for athletic performance-enhancing substances 

    Mavridis, Lazaros; Mitchell, John B. O. (2013-06-25) - Journal article
    Background: The World Anti-Doping Agency (WADA) publishes the Prohibited List, a manually compiled international standard of substances and methods prohibited in-competition, out-of-competition and in particular sports. ...
  • Quantitative and evolutionary global analysis of enzyme reaction mechanisms 

    Nath, Neetika (University of St Andrews, 2015-06-24) - Thesis
    The most widely used classification system describing enzyme-catalysed reactions is the Enzyme Commission (EC) number. Understanding enzyme function is important for both fundamental scientific and pharmaceutical reasons. ...
  • RadarCat  : Radar Categorization for input & interaction 

    Yeo, Hui Shyong; Flamich, Gergely; Schrempf, Patrick; Harris-Birtill, David Cameron Christopher; Quigley, Aaron John (ACM, 2016-10-16) - Conference item
    In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We demonstrate that we can train and ...
  • A survey of self-healing systems frameworks 

    Schneider, Christopher; Barker, Adam David; Dobson, Simon Andrew (2015-10) - Journal article
    Rising complexity within multi-tier computing architectures remains an open problem. As complexity increases, so do the costs associated with operating and maintaining systems within these environments. One approach for ...
  • Towards data-centric control of sensor networks through Bayesian dynamic linear modelling 

    Fang, Lei; Dobson, Simon Andrew (IEEE, 2015-09-21) - Conference item
    Wireless sensor networks usually operate in dynamic, stochastic environments. While the behaviour of individual nodes is important, they are better seen as contributors to a larger mission, and managing the sensing quality ...
  • Using unsupervised machine learning for fault identification in virtual machines 

    Schneider, Christopher (University of St Andrews, 2015) - Thesis
    Self-healing systems promise operating cost reductions in large-scale computing environments through the automated detection of, and recovery from, faults. However, at present there appears to be little known empirical ...