Integrating a Non-Uniformly Sampled Software Retina with a Deep CNN Model

We present a biologically inspired method for pre-processing images applied to CNNs that reduces their memory requirements while increasing their invariance to scale and rotation changes. Our method is based on the mammalian retino-cortical transform: a mapping between a pseudo-randomly tessellated retina model (used to sample an input image) and a CNN. The aim of this first pilot study is to demonstrate a functional retinaintegrated CNN implementation and this produced the following results: a network using the full retino-cortical transform yielded an F1 score of 0.80 on a test set during a 4-way classification task, while an identical network not using the proposed method yielded an F1 score of 0.86 on the same task. The method reduced the visual data by e×7, the input data to the CNN by 40% and the number of CNN training epochs by 64%. These results demonstrate the viability of our method and hint at the potential of exploiting functional traits of natural vision systems in CNNs

Towards Autopoietic Computing

A key challenge in modern computing is to develop systems that address complex, dynamic problems in a scalable and efficient way, because the increasing complexity of software makes designing and maintaining efficient and flexible systems increasingly difficult. Biological systems are thought to possess robust, scalable processing paradigms that can automatically manage complex, dynamic problem spaces, possessing several properties that may be useful in computer systems. The biological properties of self-organisation, self-replication, self-management, and scalability are addressed in an interesting way by autopoiesis, a descriptive theory of the cell founded on the concept of a system's circular organisation to define its boundary with its environment. In this paper, therefore, we review the main concepts of autopoiesis and then discuss how they could be related to fundamental concepts and theories of computation. The paper is conceptual in nature and the emphasis is on the review of other people's work in this area as part of a longer-term strategy to develop a formal theory of autopoietic computing.Comment: 10 Pages, 3 figure

Complex dynamics of elementary cellular automata emerging from chaotic rules

We show techniques of analyzing complex dynamics of cellular automata (CA) with chaotic behaviour. CA are well known computational substrates for studying emergent collective behaviour, complexity, randomness and interaction between order and chaotic systems. A number of attempts have been made to classify CA functions on their space-time dynamics and to predict behaviour of any given function. Examples include mechanical computation, \lambda{} and Z-parameters, mean field theory, differential equations and number conserving features. We aim to classify CA based on their behaviour when they act in a historical mode, i.e. as CA with memory. We demonstrate that cell-state transition rules enriched with memory quickly transform a chaotic system converging to a complex global behaviour from almost any initial condition. Thus just in few steps we can select chaotic rules without exhaustive computational experiments or recurring to additional parameters. We provide analysis of well-known chaotic functions in one-dimensional CA, and decompose dynamics of the automata using majority memory exploring glider dynamics and reactions

A Survey of Cellular Automata: Types, Dynamics, Non-uniformity and Applications

Cellular automata (CAs) are dynamical systems which exhibit complex global behavior from simple local interaction and computation. Since the inception of cellular automaton (CA) by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modelling different physical, natural as well as real-life phenomena. Classically, CAs are uniform. However, non-uniformity has also been introduced in update pattern, lattice structure, neighborhood dependency and local rule. In this survey, we tour to the various types of CAs introduced till date, the different characterization tools, the global behaviors of CAs, like universality, reversibility, dynamics etc. Special attention is given to non-uniformity in CAs and especially to non-uniform elementary CAs, which have been very useful in solving several real-life problems.Comment: 43 pages; Under review in Natural Computin

A Virtual Grain Structure Representation System for Micromechanics Simulations

Representing a grain structure within a combined finite element computer aided engineering environment is essential for micromechanics simulations. Methods are required to effectively generate high-fidelity virtual grain structures for accurate studies. A high-fidelity virtual grain structure means a statistically equivalent structure in conjunction with desired grain size distribution features, and must be represented with realistic grain morphology. A family of controlled Poisson Voronoi tessellation (CPVT) models have been developed in this work for systematically generating virtual grain structures with the aforementioned properties. Three tasks have been accomplished in the development of the CPVT models: (i) defining the grain structure’s regularity that specifies the uniformity of a tessellation as well as deriving a control parameter based on the regularity; (ii) modelling the mapping from a grain structure’s regularity to its grain size distribution; and (iii) establishing the relation between a set of physical parameters and a distribution function. A one-gamma distribution function is used to describe a grain size distribution characteristic and a group of four physical parameters are employed to represent the metallographic measurements of a grain size distribution property. Mathematical proofs of the uniqueness of the determination of the distribution parameter from the proposed set of physical parameters have been studied, and an efficient numerical procedure is provided for computing the distribution parameter. Based on the general scheme, two- and three-dimensional CPVT models have been formulated, which respectively define the quantities of regularity and control parameters, and model the mapping between regularity and grain size distribution. For the 2D-CPVT model, statistical tests have been carried out to validate the accuracy and robustness of regularity and grain size distribution control. In addition, micrographs with different grain size distribution features are employed to examine the capability of the 2D-CPVT model to generate virtual grain structures that meet physical measurements. A crystal plasticity finite element (CPFE) simulation of plane strain uniaxial tension has been performed to show the effect of grain size distribution on local strain distribution. For the 3D-CPVT model, a set of CPFE analyses of micro-pillar compression have been run and the effects of both regularity and grain size on deformation responses investigated. Further to this, a multi-zone scheme is proposed for the CPVT models to generate virtual gradient grain structures. In conjunction with the CPVT model that controls the seed generating process within individual zones, the multi-zone CPVT model has been developed by incorporating a novel mechanism of controlling the seed generation for grains spanning different zones. This model has the flexibility of generating various gradient grain structures and the natural morphology for interfacial grains between adjacent zones. Both of the 2D- and 3D-CPVT models are capable of generating a virtual grain structure with a mean grain size gradient for the grain structure domain and grain size distribution control for individual zones. A true gradient grain structure, two simulated gradient grain structure, and a true gradient grain structure with an elongated zone have been used to examine the capability of the multi-zone CPVT model. To facilitate the CPFE analyses of inter-granular crack initiation and evolution using the cohesive zone models, a Voronoi tessellation model with non-zero thickness cohesive zone representation was developed. A grain boundary offsetting algorithm is proposed to efficiently produce the cohesive boundaries for a Voronoi tessellation. The most challenging issue of automatically meshing multiple junctions with quadrilateral elements has been resolved and a rule-based method is presented to perform the automatically partitioning of cohesive zone junctions, including data representation, edge event processing and cut-trim operations. In order to demonstrate the novelty of the proposed cohesive zone modelling and junction partitioning schemes, the CPFE simulations of plane strain uniaxial tension and three point bending have been studied. A software system, VGRAIN, was developed to implement the proposed virtual grain structure modelling methods. Via user-friendly interfaces and the well-organised functional modules a virtual grain structure can be automatically generated to a very large-scale with the desired grain morphology and grain size properties. As a pre-processing grain structure representation system, VGRAIN is also capable of defining crystallographic orientations and mechanical constants for a generated grain structure. A set of additional functions has also been developed for users to study a generated grain structure and verify the feasibility of the generated case for their simulation requirements. A well-built grain structure model in VGRAIN can be easily exported into the commercial FE/CAE platform, e.g. ABAQUS and DEFORM, via script input, whereby the VGRAIN system is seamlessly integrated into CPFE modelling and simulation processing

Large strain compressive response of 2-D periodic representative volume element for random foam microstructures

A numerical investigation has been conducted to determine the influence of Representative Volume Element (RVE) size and degree of irregularity of polymer foam microstructure on its compressive mechanical properties, including stiffness, plateau stress and onset strain of densification. Periodic two-dimensional RVEs have been generated using a Voronoi-based numerical algorithm and compressed. Importantly, self-contact of the foam’s internal microstructure has been incorporated through the use of shell elements, allowing simulation of the foam well into the densification stage of compression; strains of up to 80 percent are applied. Results suggest that the stiffness of the foam RVE is relatively insensitive to RVE size but tends to soften as the degree of irregularity increases. Both the shape of the plateau stress and the onset strain of densification are sensitive to both the RVE size and degree of irregularity. Increasing the RVE size and decreasing the degree of irregularity both tend to result in a decrease of the gradient of the plateau region, while increasing the RVE size and degree of irregularity both tend to decrease the onset strain of densification. Finally, a method of predicting the onset strain of densification to an accuracy of about 10 per cent, while reducing the computational cost by two orders of magnitude is suggested

A space-variant visual pathway model for data efficient deep learning

We present an investigation into adopting a model of the retino-cortical mapping, found in biological visual systems, to improve the efficiency of image analysis using Deep Convolutional Neural Nets (DCNNs) in the context of robot vision and egocentric perception systems. This work has now enabled DCNNs to process input images approaching one million pixels in size, in real time, using only consumer grade graphics processor (GPU) hardware in a single pass of the DCNN