How Will the Emerging Plurality of Lives Change How We Conceive of and Relate to Life?

The project “A Plurality of Lives” was funded and hosted by the Pufendorf Institute for Advanced Studies at Lund University, Sweden. The aim of the project was to better understand how a second origin of life, either in the form of a discovery of extraterrestrial life, life developed in a laboratory, or machines equipped with abilities previously only ascribed to living beings, will change how we understand and relate to life. Because of the inherently interdisciplinary nature of the project aim, the project took an interdisciplinary approach with a research group made up of 12 senior researchers representing 12 different disciplines. The project resulted in a joint volume, an international symposium, several new projects, and a network of researchers in the field, all continuing to communicate about and advance the aim of the project

Synthetic organisms and living machines: Positioning the products of synthetic biology at the borderline between living and non-living matter

The difference between a non-living machine such as a vacuum cleaner and a living organism as a lion seems to be obvious. The two types of entities differ in their material consistence, their origin, their development and their purpose. This apparently clear-cut borderline has previously been challenged by fictitious ideas of “artificial organism” and “living machines” as well as by progress in technology and breeding. The emergence of novel technologies such as artificial life, nanobiotechnology and synthetic biology are definitely blurring the boundary between our understanding of living and non-living matter. This essay discusses where, at the borderline between living and non-living matter, we can position the future products of synthetic biology that belong to the two hybrid entities “synthetic organisms” and “living machines” and how the approaching realization of such hybrid entities affects our understanding of organisms and machines. For this purpose we focus on the description of three different types of synthetic biology products and the aims assigned to their realization: (1) synthetic minimal cells aimed at by protocell synthetic biology, (2) chassis organisms strived for by synthetic genomics and (3) genetically engineered machines produced by bioengineering. We argue that in the case of synthetic biology the purpose is more decisive for the categorization of a product as an organism or a machine than its origin and development. This has certain ethical implications because the definition of an entity as machine seems to allow bypassing the discussion about the assignment and evaluation of instrumental and intrinsic values, which can be raised in the case of organisms

Semantic Mapping of Road Scenes

The problem of understanding road scenes has been on the fore-front in the computer vision community for the last couple of years. This enables autonomous systems to navigate and understand the surroundings in which it operates. It involves reconstructing the scene and estimating the objects present in it, such as ‘vehicles’, ‘road’, ‘pavements’ and ‘buildings’. This thesis focusses on these aspects and proposes solutions to address them. First, we propose a solution to generate a dense semantic map from multiple street-level images. This map can be imagined as the bird’s eye view of the region with associated semantic labels for ten’s of kilometres of street level data. We generate the overhead semantic view from street level images. This is in contrast to existing approaches using satellite/overhead imagery for classification of urban region, allowing us to produce a detailed semantic map for a large scale urban area. Then we describe a method to perform large scale dense 3D reconstruction of road scenes with associated semantic labels. Our method fuses the depth-maps in an online fashion, generated from the stereo pairs across time into a global 3D volume, in order to accommodate arbitrarily long image sequences. The object class labels estimated from the street level stereo image sequence are used to annotate the reconstructed volume. Then we exploit the scene structure in object class labelling by performing inference over the meshed representation of the scene. By performing labelling over the mesh we solve two issues: Firstly, images often have redundant information with multiple images describing the same scene. Solving these images separately is slow, where our method is approximately a magnitude faster in the inference stage compared to normal inference in the image domain. Secondly, often multiple images, even though they describe the same scene result in inconsistent labelling. By solving a single mesh, we remove the inconsistency of labelling across the images. Also our mesh based labelling takes into account of the object layout in the scene, which is often ambiguous in the image domain, thereby increasing the accuracy of object labelling. Finally, we perform labelling and structure computation through a hierarchical robust PN Markov Random Field defined on voxels and super-voxels given by an octree. This allows us to infer the 3D structure and the object-class labels in a principled manner, through bounded approximate minimisation of a well defined and studied energy functional. In this thesis, we also introduce two object labelled datasets created from real world data. The 15 kilometre Yotta Labelled dataset consists of 8,000 images per camera view of the roadways of the United Kingdom with a subset of them annotated with object class labels and the second dataset is comprised of ground truth object labels for the publicly available KITTI dataset. Both the datasets are available publicly and we hope will be helpful to the vision research community

Natural scene statistics and the development of the primary visual cortex

Vision is the dominant human sensory modality. Due to the relative ease with which both visual input and visual brain areas can be studied and manipulated, vision has become an important window for enlarging our understanding of the biological sensory processing. Whether artificial or biological, visual processing systems must quickly and efficiently make sense of a large volume of noisy, high-dimensional input. To do this they construct statistical models of the input and utilise these models to efficiently encode visual scenes, detect features and construct a model of the world. In this thesis, we combine the study of natural scene statistics with mathematical models, experimental analysis and visual psychophysics to glean a deeper understanding of the development and function of the mammalian primary visual cortex. We start by considering functional models of receptive field development. We find, in agreement with previous work, that unsupervised learning models trained on natural scenes consistently learn that oriented ``edges'' (Gabor-like filters) are the basic features of natural scenes. The similarity between these filters and primary visual cortex receptive fields is strong evidence that primary visual cortex receptive fields are optimal encoders of visual input. We then significantly extend this work by comparing the prediction of unsupervised learning models with the receptive fields of animals reared in unusual visual environments. We find good agreement, which is evidence that aspects of receptive fields are learned during development, rather than innate. We also show that applying such unsupervised learning models to binocular visual input is not a simple extension of monocular visual input. Inter-ocular correlations change the optimal encoding strategy of binocular input so that it depends on edge orientation. Such functional models intriguingly predict an over-representation of vertically oriented receptive fields. After establishing that oriented edges are the basic feature of natural scenes and the unit of primary visual cortex receptive fields, we consider the statistics of edge arrangements in natural scenes. \citet{Sigman2001} showed that edges in natural scenes over short distances tend to be tangent to a common circle, or co-circular. Edge arrangements which contain a dependence between edge position and orientation may be said to have ``reduced symmetry'' as they lack a symmetry in that the edge position and orientation cannot be rotated independently without modifying the statistics of the arrangement. Co-circularity is one specific type of reduced symmetry. We extend previous work on natural scene co-circularity using a noise-resistant measure of co-circularity we develop and show that natural scenes contain significant co-circularity over extremely large angular distances ($>14\degree$). We also discuss preliminary work into variations in co-circularity statistics by scene type. After establishing that co-circularity is found pervasively in natural scenes, even over large distances, we then return to the structure of the primary visual cortex, but this time at the network level. Previous work has shown that, like edges in natural scenes, V1 orientation preferences maps also have reduced symmetry. However, the details of this dependence between orientation and position have not been examined in detail. We examine cat orientation preference maps from normal, stripe and blind-reared animals and find that, although orientation preference maps do contain reduced symmetry, it is not co-circularity. Moreover, the statistics of reduced symmetry in the maps are not affected by changes to visual input during development. Continuing our examination of V1 network structure, we consider the statistics of lateral connectivity in tree shrew V1. Previous work demonstrated that long-range V1 lateral connections are more common between regions with similar orientation preferences \citep{Bosking1997}. We re-examine this connectivity data using our noise-resistance measure of co-circularity. We find evidence that lateral connections between cells in the primary visual cortex may use two opposite wiring strategies which simultaneously facilitate quick processing of co-circular visual input while increasing the salience of the less expected deviations from co-circularity. Finally, we use the psychophysics of binocular rivalry to test whether co-circularity statistics can affect the functional processing of visual input in humans. We show, using binocular rivalry dominance as an objective measure of salience, that randomly arranged edges are more salient than edge arrangements which contain co-circularity. This is evidence that early visual processing may be functionally utilising edge arrangement statistics. In concurrence with our findings about lateral connections, this may indicate a general strategy of increasing the salience of unexpected visual input. Overall, we demonstrate that early visual coding uses natural scene statistics extensively. We show that oriented edges are a key currency in early visual processing. We find that the arrangement of edges in natural scenes contain rich statistical structure which influences wiring in the primary visual cortex during development and produces measurable changes in the salience of visual stimuli

Evolutionary synthesis of analog networks

The significant increase in the available computational power that took place in recent decades has been accompanied by a growing interest in the application of the evolutionary approach to the synthesis of many kinds of systems and, in particular, to the synthesis of systems like analog electronic circuits, neural networks, and, more generally, autonomous systems, for which no satisfying systematic and general design methodology has been found to date. Despite some interesting results in the evolutionary synthesis of these kinds of systems, the endowment of an artificial evolutionary process with the potential for an appreciable increase of complexity of the systems thus generated appears still an open issue. In this thesis the problem of the evolutionary growth of complexity is addressed taking as starting point the insights contained in the published material reporting the unfinished work done in the late 1940s and early 1950s by John von Neumann on the theory of self-reproducing automata. The evolutionary complexity-growth conditions suggested in that work are complemented here with a series of auxiliary conditions inspired by what has been discovered since then relatively to the structure of biological systems, with a particular emphasis on the workings of genetic regulatory networks seen as the most elementary, full-fledged level of organization of existing living organisms. In this perspective, the first chapter is devoted to the formulation of the problem of the evolutionary growth of complexity, going from the description of von Neumann's complexity-growth conditions to the specification of a set of auxiliary complexity-growth conditions derived from the analysis of the operation of genetic regulatory networks. This leads to the definition of a particular structure for the kind of systems that will be evolved and to the specification of the genetic representation for them. A system with the required structure — for which the name analog network is suggested — corresponds to a collection of devices whose terminals are connected by links characterized by a scalar value of interaction strength. One of the specificities of the evolutionary system defined in this thesis is the way these values of interaction strength are determined. This is done by associating with each device terminal of the evolving analog network a sequence of characters extracted from the sequences that constitute the genome representing the network, and by defining a map from pairs of sequences of characters to values of interaction strength. Whereas the first chapter gives general prescriptions for the definition of an evolutionary system endowed with the desired complexity-growth potential, the second chapter is devoted to the specification of all the details of an actual implementation of those prescriptions. In this chapter the structure of the genome and of the corresponding genetic operators are defined. A technique for the genetic encoding of the devices constituting the analog network is described, along with a way to implement the map that specifies the interaction between the devices of the evolved system, and between them and the devices constituting the external environment of the evolved system. The proposed implementation of the interaction map is based on the local alignment of sequences of characters. It is shown how the parameters defining the local alignment can be chosen, and what strategies can be adopted to prevent the proliferation of unwanted interactions. The third chapter is devoted to the application of the evolutionary system defined in the second chapter to problems aimed at assessing the suitability in an evolutionary context of the local alignment technique and to problems aimed at assessing the evolutionary potential of the complete evolutionary system when applied to the synthesis of analog networks. Finally, the fourth chapter briefly considers some further questions that are relevant to the proposed approach but could not be addressed in the context of this thesis. A series of appendixes is devoted to some complementary issues: the definition of a measure of diversity for an evolutionary population employing the genetic description introduced in this thesis; the choice of the quantizer for the values of interaction strength between the devices constituting the evolved analog network; the modifications required to use the analog electronic circuit simulator SPICE as a simulation engine for an evolutionary or an optimization process

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance