The Translocal Event and the Polyrhythmic Diagram

This thesis identifies and analyses the key creative protocols in translocal performance practice, and ends with suggestions for new forms of transversal live and mediated performance practice, informed by theory. It argues that ontologies of emergence in dynamic systems nourish contemporary practice in the digital arts. Feedback in self-organised, recursive systems and organisms elicit change, and change transforms. The arguments trace concepts from chaos and complexity theory to virtual multiplicity, relationality, intuition and individuation (in the work of Bergson, Deleuze, Guattari, Simondon, Massumi, and other process theorists). It then examines the intersection of methodologies in philosophy, science and art and the radical contingencies implicit in the technicity of real-time, collaborative composition. Simultaneous forces or tendencies such as perception/memory, content/ expression and instinct/intellect produce composites (experience, meaning, and intuition- respectively) that affect the sensation of interplay. The translocal event is itself a diagram - an interstice between the forces of the local and the global, between the tendencies of the individual and the collective. The translocal is a point of reference for exploring the distribution of affect, parameters of control and emergent aesthetics. Translocal interplay, enabled by digital technologies and network protocols, is ontogenetic and autopoietic; diagrammatic and synaesthetic; intuitive and transductive. KeyWorx is a software application developed for realtime, distributed, multimodal media processing. As a technological tool created by artists, KeyWorx supports this intuitive type of creative experience: a real-time, translocal “jamming” that transduces the lived experience of a “biogram,” a synaesthetic hinge-dimension. The emerging aesthetics are processual – intuitive, diagrammatic and transversal

Corticonic models of brain mechanisms underlying cognition and intelligence

The concern of this review is brain theory or more specifically, in its first part, a model of the cerebral cortex and the way it:(a) interacts with subcortical regions like the thalamus and the hippocampus to provide higher-level-brain functions that underlie cognition and intelligence, (b) handles and represents dynamical sensory patterns imposed by a constantly changing environment, (c) copes with the enormous number of such patterns encountered in a lifetime bymeans of dynamic memory that offers an immense number of stimulus-specific attractors for input patterns (stimuli) to select from, (d) selects an attractor through a process of “conjugation” of the input pattern with the dynamics of the thalamo–cortical loop, (e) distinguishes between redundant (structured)and non-redundant (random) inputs that are void of information, (f) can do categorical perception when there is access to vast associative memory laid out in the association cortex with the help of the hippocampus, and (g) makes use of “computation” at the edge of chaos and information driven annealing to achieve all this. Other features and implications of the concepts presented for the design of computational algorithms and machines with brain-like intelligence are also discussed. The material and results presented suggest, that a Parametrically Coupled Logistic Map network (PCLMN) is a minimal model of the thalamo–cortical complex and that marrying such a network to a suitable associative memory with re-entry or feedback forms a useful, albeit, abstract model of a cortical module of the brain that could facilitate building a simple artificial brain. In the second part of the review, the results of numerical simulations and drawn conclusions in the first part are linked to the most directly relevant works and views of other workers. What emerges is a picture of brain dynamics on the mesoscopic and macroscopic scales that gives a glimpse of the nature of the long sought after brain code underlying intelligence and other higher level brain functions. Physics of Life Reviews 4 (2007) 223–252 © 2007 Elsevier B.V. All rights reserved

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Applied Information Systems Research Program (AISRP). Workshop 2: Meeting Proceedings

The Earth and space science participants were able to see where the current research can be applied in their disciplines and computer science participants could see potential areas for future application of computer and information systems research. The Earth and Space Science research proposals for the High Performance Computing and Communications (HPCC) program were under evaluation. Therefore, this effort was not discussed at the AISRP Workshop. OSSA's other high priority area in computer science is scientific visualization, with the entire second day of the workshop devoted to it

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Artificial Ontogenies: A Computational Model of the Control and Evolution of Development

Understanding the behaviour of biological systems is a challenging task. Gene regulation, development and evolution are each a product of nonlinear interactions between many individual agents: genes, cells or organisms. Moreover, these three processes are not isolated, but interact with one another in an important fashion. The development of an organism involves complex patterns of dynamic behaviour at the genetic level. The gene networks that produce this behaviour are subject to mutations that can alter the course of development, resulting in the production of novel morphologies. Evolution occurs when these novel morphologies are favoured by natural selection and survive to pass on their genes to future generations. Computational models can assist us to understand biological systems by providing a framework within which their behaviour can be explored. Many natural processes, including gene regulation and development, have a computational element to their control. Constructing formal models of these systems enables their behaviour to be simulated, observed and quantified on a scale not otherwise feasible. This thesis uses a computational simulation methodology to explore the relationship between development and evolution. An important question in evolutionary biology is how to explain the direction of evolution. Conventional explanations of evolutionary history have focused on the role of natural selection in orienting evolution. More recently, it has been argued that the nature of development, and the way it changes in response to mutation, may also be a significant factor. A network-lineage model of artificial ontogenies is described that incorporates a developmental mapping between the dynamics of a gene network and a cell lineage representation of a phenotype. Three series of simulation studies are reported, exploring: (a) the relationship between the structure of a gene network and its dynamic behaviour; (b) the characteristic distributions of ontogenies and phenotypes generated by the dynamics of gene networks; (c) the effect of these characteristic distributions on the evolution of ontogeny. The results of these studies indicate that the model networks are capable of generating a diverse range of stable behaviours, and possess a small yet significant sensitivity to perturbation. In the context of developmental control, the intrinsic dynamics of the model networks predispose the production of ontogenies with a modular, quasi-systematic structure. This predisposition is reflected in the structure of variation available for selection in an adaptive search process, resulting in the evolution of ontogenies biased towards simplicity. These results suggest a possible explanation for the levels of ontogenetic complexity observed in biological organisms: that they may be a product of the network architecture of developmental control. By quantifying complexity, variation and bias, the network-lineage model described in this thesis provides a computational method for investigating the effects of development on the direction of evolution. In doing so, it establishes a viable framework for simulating computational aspects of complex biological systems

A methodology for approximating motivation-related latent states in large scale scenarios: and its role in engagement prediction within a video game setting

Motivation is a fundamental psychological process guiding our everyday behaviour. For doing so, it heavily relies on the ability to attribute relevance to potentially rewarding objects and actions (i.e., incentives). However, despite its importance, quantifying the saliency that an individual might attribute to an object or an action is not an easy task, especially if done in naturalistic contexts. In this view, this thesis aims to outline a methodology for approximating the amount of attributed incentive salience in situations where large volumes of behavioural data are available but no experimental control is possible. Leveraging knowledge derived from theoretical and computational accounts of incentive salience attribution, we designed an Artificial Neural Network (ANN) tasked to infer a latent representation able to predict duration and intensity of future interactions between individuals and a series of video games. We found video games to be the ideal context for developing such methodology due to their reliance on reward mechanics and their ability to provide ecologically robust behavioural measures at scale. We developed and tested our methodology on a series of large-scale ($N> 10^6$) longitudinal datasets evaluating the ability of the generated latent representation to approximate some functional properties of attributed incentive salience. The present work opens with an overview of the concept of motivation and its interconnection with engagement in a video-game setting. It proceeds by formulating the theoretical and computation foundations on which our methodology is built upon. It then describes the iterative process of model building, evaluation and expansion underlying the implementation of our methodology. It continues by analysing the latent representation generated by the ANN and comparing its functional characteristics with those of attributed incentive salience. The manuscript ends with a general overview of the potential applications of our methodology with a particular focus on the area of automated engagement prediction and quantification in videogames settings

Nonlinear Time-Frequency Control Theory with Applications

Nonlinear control is an important subject drawing much attention. When a nonlinear system undergoes route-to-chaos, its response is naturally bounded in the time-domain while in the meantime becoming unstably broadband in the frequency-domain. Control scheme facilitated either in the time- or frequency-domain alone is insufficient in controlling route-to-chaos, where the corresponding response deteriorates in the time and frequency domains simultaneously. It is necessary to facilitate nonlinear control in both the time and frequency domains without obscuring or misinterpreting the true dynamics. The objective of the dissertation is to formulate a novel nonlinear control theory that addresses the fundamental characteristics inherent of all nonlinear systems undergoing route-to-chaos, one that requires no linearization or closed-form solution so that the genuine underlying features of the system being considered are preserved. The theory developed herein is able to identify the dynamic state of the system in real-time and restrain time-varying spectrum from becoming broadband. Applications of the theory are demonstrated using several engineering examples including the control of a non-stationary Duffing oscillator, a 1-DOF time-delayed milling model, a 2-DOF micro-milling system, unsynchronized chaotic circuits, and a friction-excited vibrating disk. Not subject to all the mathematical constraint conditions and assumptions upon which common nonlinear control theories are based and derived, the novel theory has its philosophical basis established in the simultaneous time-frequency control, on-line system identification, and feedforward adaptive control. It adopts multi-rate control, hence enabling control over nonstationary, nonlinear response with increasing bandwidth ? a physical condition oftentimes fails the contemporary control theories. The applicability of the theory to complex multi-input-multi-output (MIMO) systems without resorting to mathematical manipulation and extensive computation is demonstrated through the multi-variable control of a micro-milling system. The research is of a broad impact on the control of a wide range of nonlinear and chaotic systems. The implications of the nonlinear time-frequency control theory in cutting, micro-machining, communication security, and the mitigation of friction-induced vibrations are both significant and immediate