Mechanisms of place recognition and path integration based on the insect visual system

Animals are often able to solve complex navigational tasks in very challenging terrain, despite using low resolution sensors and minimal computational power, providing inspiration for robots. In particular, many species of insect are known to solve complex navigation problems, often combining an array of different behaviours (Wehner et al., 1996; Collett, 1996). Their nervous system is also comparatively simple, relative to that of mammals and other vertebrates. In the first part of this thesis, the visual input of a navigating desert ant, Cataglyphis velox, was mimicked by capturing images in ultraviolet (UV) at similar wavelengths to the ant’s compound eye. The natural segmentation of ground and sky lead to the hypothesis that skyline contours could be used by ants as features for navigation. As proof of concept, sky-segmented binary images were used as input for an established localisation algorithm SeqSLAM (Milford and Wyeth, 2012), validating the plausibility of this claim (Stone et al., 2014). A follow-up investigation sought to determine whether using the sky as a feature would help overcome image matching problems that the ant often faced, such as variance in tilt and yaw rotation. A robotic localisation study showed that using spherical harmonics (SH), a representation in the frequency domain, combined with extracted sky can greatly help robots localise on uneven terrain. Results showed improved performance to state of the art point feature localisation methods on fast bumpy tracks (Stone et al., 2016a). In the second part, an approach to understand how insects perform a navigational task called path integration was attempted by modelling part of the brain of the sweat bee Megalopta genalis. A recent discovery that two populations of cells act as a celestial compass and visual odometer, respectively, led to the hypothesis that circuitry at their point of convergence in the central complex (CX) could give rise to path integration. A firing rate-based model was developed with connectivity derived from the overlap of observed neural arborisations of individual cells and successfully used to build up a home vector and steer an agent back to the nest (Stone et al., 2016b). This approach has the appeal that neural circuitry is highly conserved across insects, so findings here could have wide implications for insect navigation in general. The developed model is the first functioning path integrator that is based on individual cellular connections

Classical Algebraic Geometry

Progress in algebraic geometry usually comes through the introduction of new tools and ideas to tackle the classical problems of the field. Examples include new invariants that capture some aspect of geometry in a novel way, such as Voisin’s “existence of decomposition of the diagonal”, and the extension of the class of geometric objects considered to allow constructions not previously possible, such as stacks, tropical geometry, and log structures. Many famous old problems and outstanding conjectures have been resolved in this way over the last 50 years. While the new theories are sometimes studied for their own sake, they are in the end best understood in the context of the classical questions they illuminate. The goal of the workshop was to study new developments in algebraic geometry, in the context of their application to the classical problems

Adaptive Sampling For Efficient Online Modelling

This thesis examines methods enabling autonomous systems to make active sampling and planning decisions in real time. Gaussian Process (GP) regression is chosen as a framework for its non-parametric approach allowing flexibility in unknown environments. The first part of the thesis focuses on depth constrained full coverage bathymetric surveys in unknown environments. Algorithms are developed to find and follow a depth contour, modelled with a GP, and produce a depth constrained boundary. An extension to the Boustrophedon Cellular Decomposition, Discrete Monotone Polygonal Partitioning is developed allowing efficient planning for coverage within this boundary. Efficient computational methods such as incremental Cholesky updates are implemented to allow online Hyper Parameter optimisation and fitting of the GP's. This is demonstrated in simulation and the field on a platform built for the purpose. The second part of this thesis focuses on modelling the surface salinity profiles of estuarine tidal fronts. The standard GP model assumes evenly distributed noise, which does not always hold. This can be handled with Heteroscedastic noise. An efficient new method, Parametric Heteroscedastic Gaussian Process regression, is proposed. This is applied to active sample selection on stationary fronts and adaptive planning on moving fronts where a number of information theoretic methods are compared. The use of a mean function is shown to increase the accuracy of predictions whilst reducing optimisation time. These algorithms are validated in simulation. Algorithmic development is focused on efficient methods allowing deployment on platforms with constrained computational resources. Whilst the application of this thesis is Autonomous Surface Vessels, it is hoped the issues discussed and solutions provided have relevance to other applications in robotics and wider fields such as spatial statistics and machine learning in general

Molecular dynamics study of the allosteric control mechanisms of the glycolytic pathway

There is a growing body of interest to understand the regulation of allosteric proteins. Allostery is a phenomenon of protein regulation whereby binding of an effector molecule at a remote site affects binding and activity at the protein‟s active site. Over the years, these sites have become popular drug targets as they provide advantages in terms of selectivity and saturability. Both experimental and computational methods are being used to study and identify allosteric sites. Although experimental methods provide us with detailed structures and have been relatively successful in identifying these sites, they are subject to time and cost limitations. In the present dissertation, Molecular Dynamics Simulations (MDS) and Principal Component Analysis (PCA) have been employed to enhance our understanding ofallostery and protein dynamics. MD simulations generated trajectories which were then qualitatively assessed using PCA. Both of these techniques were applied to two important trypanosomatid drug targets and controlling enzymes of the glycolytic pathway - pyruvate kinase (PYK) and phosphofructokinase (PFK). Molecular Dynamics simulations were first carried out on both the effector bound and unbound forms of the proteins. This provided a framework for direct comparison and inspection of the conformational changes at the atomic level. Following MD simulations, PCA was run to further analyse the motions. The principal components thus captured are in quantitative agreement with the previously published experimental data which increased our confidence in the reliability of our simulations. Also, the binding of FBP affects the allosteric mechanism of PYK in a very interesting way. The inspection of the vibrational modes reveals interesting patterns in the movement of the subunits which differ from the conventional symmetrical pattern. Also, lowering of B-factors on effector binding provides evidence that the effector is not only locking the R-state but is also acting as a general heat-sink to cool down the whole tetramer. This observation suggests that protein rigidity and intrinsic heat capacity are important factors in stabilizing allosteric proteins. Thus, this work also provides new and promising insights into the classical Monod-Wyman-Changeux model of allostery

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Theory and Metatheory of Atemporal Primacy

This thesis-on macro-ontology, physics, logic, and metalogical principles presents the findings, results, theorems, and metatheory that correct long-standing defects and deficiencies of current standard model (SM) physics and cosmology. It eliminates artificial SM anomalies, paradoxes, logical fallacies, absurdities, and conflicts with reality (and the findings of plasma physics, astronomy, ontology, epistemics, etc.). New theorems and metatheorems eliminate the illogic maintaining distortions of post-Einsteinian physics, its wildly speculative conjectures, and shibboleths (its unrealistic assumptions commonly accepted as facts). In critiques of misperceptions, misconceptions and misinterpretations of observations and data, this work exposes the common failure to recognize the severe deficiencies of theory, science, ontology, and metamathematics. New results prove that chronic failure to differentiate realities from theorems, hypotheses, conjectures, opinions, and beliefs prevents progress to better physics and STEM education. Therefore, this work also enables resolution of the long-standing logical incongruency of physics, psychology, cognitive science, and philosophy, especially ontology. So, the facts and proofs presented here can enable a new era of discovery, creativity, and technological progress

Theory and Metatheory of Atemporal Primacy

This thesis-on macro-ontology, physics, logic, and metalogical principles presents the findings, results, theorems, and metatheory that correct long-standing defects and deficiencies of current standard model (SM) physics and cosmology. It eliminates artificial SM anomalies, paradoxes, logical fallacies, absurdities, and conflicts with reality (and the findings of plasma physics, astronomy, ontology, epistemics, etc.). New theorems and metatheorems eliminate the illogic maintaining distortions of post-Einsteinian physics, its wildly speculative conjectures, and shibboleths (its unrealistic assumptions commonly accepted as facts). In critiques of misperceptions, misconceptions and misinterpretations of observations and data, this work exposes the common failure to recognize the severe deficiencies of theory, science, ontology, and metamathematics. New results prove that chronic failure to differentiate realities from theorems, hypotheses, conjectures, opinions, and beliefs prevents progress to better physics and STEM education. Therefore, this work also enables resolution of the long-standing logical incongruency of physics, psychology, cognitive science, and philosophy, especially ontology. So, the facts and proofs presented here can enable a new era of discovery, creativity, and technological progress