Design and Characterisation of a Novel Artificial Life System Incorporating Hierarchical Selection

In this thesis, a minimal artificial chemistry system is presented, which is inspired by the RNA World hypothesis and is loosely based on Holland's Learning Classier Systems. The Molecular Classier System (MCS) takes a bottom-up, individual-based approach to building artificial bio-chemical networks. The MCS has been developed to demonstrate the effects of hierarchical selection. Hierarchical selection appears to have been critical for the evolution of complexity in life as we know it yet, to date, no computational artificial life system has investigated the viability of using hierarchical selection as a mechanism for achieving qualitatively similar results. Hierarchy in MCS is enforced by constraining artificial molecules, which are modeled as individuals, to exist within externally provided containers - protocells. This research is focused on the period of time surrounding the conjectured first Major Transition - from individual replicating molecules to populations of molecules existing within cells. Protocells can be thought of as simplified versions of contemporary biological cells. Molecular replication within these protocells causes them to grow until they undergo a process of binary fission. Darwinian selection is continuously and independently applied at both the molecular level and the protocell level. Experimental results are presented which display the phenomenon of selectional stalemate where the selectional pressures are applied in opposite directions such that they meet in the middle. The work culminates with the presentation of a stable artificial protocell system which is capable of demonstrating ongoing evolution at the protocell level via hierarchical selection of molecular species. Supplementary results are presented in the Appendix material as a set of experiments where selectional pressure is applied at the protocell level in a manner that indirectly favours particular artificial bio-chemical networks at the molecular level. It is shown that a molecular trait which serves no useful purpose to the molecules when they are not contained within protocells is exploited for the benefit of the collective once the molecules are constrained to live together. It is further shown that through the mechanism of hierarchical selection, the second-order effects of this molecular trait can be used by evolution to distinguish between protocells which contain desirable networks, and those that do not. A treatment of the computational potential of such a mechanism is presented with special attention given to the idea that such computation may indeed form the basis for the later evolution of the complicated Cell Signaling Pathways that are exhibited by modern cells

Human motion reconstruction using wearable accelerometers

We address the problem of capturing human motion in scenarios where the use of a traditional optical motion capture system is impractical. Such scenarios are relatively commonplace, such as in large spaces, outdoors or at competitive sporting events, where the limitations of such systems are apparent: the small physical area where motion capture can be done and the lack of robustness to lighting changes and occlusions. In this paper, we advocate the use of body-worn wearable wireless accelerometers for reconstructing human motion and to this end we outline a system that is more portable than traditional optical motion capture systems, whilst producing naturalistic motion. Additionally, if information on the person's root position is available, an extended version of our algorithm can use this information to correct positional drift

An approach to evolving cell signaling networks in silico

Cell Signaling Networks(CSN) are complex bio-chemical networks which, through evolution, have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. From a computational point of view, modeling Artificial Cell Signaling Networks (ACSNs) in silico may provide new ways to design computer systems which may have specialized application areas. To investigate these new opportunities, we review the key issues of modeling ACSNs identified as follows. We first present an analogy between analog and molecular computation. We discuss the application of evolutionary techniques to evolve biochemical networks for computational purposes. The potential roles of crosstalk in CSNs are then examined. Finally we present how artificial CSNs can be used to build robust real-time control systems. The research we are currently involved in is part of the multi disciplinary EU funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs. This also complements the present requirements of Computational Systems Biology by providing new insights in micro-biology research

TennisSense: a platform for extracting semantic information from multi-camera tennis data

In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface

Automatic camera selection for activity monitoring in a multi-camera system for tennis

In professional tennis training matches, the coach needs to be able to view play from the most appropriate angle in order to monitor players' activities. In this paper, we describe and evaluate a system for automatic camera selection from a network of synchronised cameras within a tennis sporting arena. This work combines synchronised video streams from multiple cameras into a single summary video suitable for critical review by both tennis players and coaches. Using an overhead camera view, our system automatically determines the 2D tennis-court calibration resulting in a mapping that relates a player's position in the overhead camera to their position and size in another camera view in the network. This allows the system to determine the appearance of a player in each of the other cameras and thereby choose the best view for each player via a novel technique. The video summaries are evaluated in end-user studies and shown to provide an efficient means of multi-stream visualisation for tennis player activity monitoring

Combining inertial and visual sensing for human action recognition in tennis

In this paper, we present a framework for both the automatic extraction of the temporal location of tennis strokes within a match and the subsequent classification of these as being either a serve, forehand or backhand. We employ the use of low-cost visual sensing and low-cost inertial sensing to achieve these aims, whereby a single modality can be used or a fusion of both classification strategies can be adopted if both modalities are available within a given capture scenario. This flexibility allows the framework to be applicable to a variety of user scenarios and hardware infrastructures. Our proposed approach is quantitatively evaluated using data captured from elite tennis players. Results point to the extremely accurate performance of the proposed approach irrespective of input modality configuration

Preliminary steps toward artificial protocell computation

Protocells are hypothesised as a transitional phase in the origin of life, prior to the evolution of fully functional prokaryotic cells. The work reported here is being done in the context of the PACE project, which is investigating the fabrication of artificial protocells de novo. We consider here the important open question of whether or how articifial protocells (if or when they are successfully fabricated) might be applied as “computing” devices—what sort of computing might they be suitable for, and how might they be “programmed”? We also present some preliminary analysis of a crude model of such “evolutionary protocell computation”

On Protocell "Computation"

The EU FP6 Integrated Project PACE ('Programmable Artificial Cell Evolution') is investigating the creation, de novo, of chemical 'protocells'. These will be minimal 'wetware' chemical systems integrating molecular information carriers, primitive energy conversion (metabolism) and containment (membrane). Ultimately they should be capable of autonomous reproduction, and be 'programmable' to realise specific desired function. A key objective of PACE is to explore the application of such protocell technology to build novel nanoscale computational devices. In principle, such computation might be realised either at the level of an individual protocell or at the level of self-assembling, multi-cellular, aggregates. In the case of the individual protocell level, a form of 'molecular computation' may be possible in the manner of 'cell signalling networks' in modern cells. This might be particularly appropriate where a protocell is deployed to interface directly with molecular systems, such as in 'smart drug' applications. 'Programming' of molecular computation functionality might be realised by evolutionary techniques, i.e., applying selection to polulations of (reproducing) protocells. Reflexive string rewriting systems may provide an appropriate formal model of molecular computation. The behaviour of minimal reflexive string rewriting systems, incorporated in reproducing containers (protocells), is being explored in simulation. This is a basis for possible design of minimal protocell 'computers'

Anti-social behavior detection in audio-visual surveillance systems

In this paper we propose a general purpose framework for detection of unusual events. The proposed system is based on the unsupervised method for unusual scene detection in web{cam images that was introduced in [1]. We extend their algorithm to accommodate data from different modalities and introduce the concept of time-space blocks. In addition, we evaluate early and late fusion techniques for our audio-visual data features. The experimental results on 192 hours of data show that data fusion of audio and video outperforms using a single modality

Dublin City University at TRECVID 2008

In this paper we describe our system and experiments performed for both the automatic search task and the event detection task in TRECVid 2008. For the automatic search task for 2008 we submitted 3 runs utilizing only visual retrieval experts, continuing our previous work in examining techniques for query-time weight generation for data-fusion and determining what we can get from global visual only experts. For the event detection task we submitted results for 5 required events (ElevatorNoEntry, OpposingFlow, PeopleMeet, Embrace and PersonRuns) and 1 optional event (DoorOpenClose)