A generative nervous system for the planet

Generative systems are now being proposed for addressing major ecological problems. The Complex Urban Systems Project (CUSP) founded in 2008 at the Queensland University of Technology, emphasises the ecological significance of the generative global networking of urban environments. It argues that the natural planetary systems for balancing global ecology are no longer able to respond sufficiently rapidly to the ecological damage caused by humankind and by dense urban conurbations in particular as evidenced by impacts such as climate change. The proposal of this research project is to provide a high speed generative nervous system for the planet by connecting major cities globally to interact directly with natural ecosystems to engender rapid ecological response. This would be achieved by active interactions of the global urban network with the natural ecosystem in the ecological principle of entropy. The key goal is to achieve ecologically positive cities by activating self-organising cities capable of full integration into natural eco-systems and to netowork the cities globally to provide the planet with a nervous system.

Meaningful musical engagement : the potential impact of generative music systems on learning experiences

In order to create music, the student must establish a relationship with the musical materials. In this thesis, I examine the capacity of a generative music system called jam2jam to offer individuals a virtual musical play-space to explore. I outline the development of an iteration of software development named jam2jam blue and the evolution of a games-like user interface in the research design that jointly revealed the nature of this musical exploration. The findings suggest that the jam2jam blue interface provided an expressive gestural instrument to jam and experience musicmaking. By using the computer as an instrument, participants in this study were given access to meaningful musical experiences in both solo and ensemble situations and the researcher is allowed a view of their development of a relationship with the musical materials from the perspective of the individual participants. Through an iterative software development methodology, pedagogy and experience design were created simultaneously. The research reveals the potential for the jam2jam software to be used as a reflective tool for feedback and assessment purposes. The power of access to ensemble music making is realised though the participants’ virtual experiences which are brought into their physical space by sharing their experience with others. It is suggested that this interaction creates an environment conducive to self-initiated learning in which music is the language of interaction. The research concludes that the development of a relationship between the explorer and the musical materials is subject to the collaborative nature of the interaction through which the music is experienced.

Parametric and generative methods with building information modelling : connecting BIM with explorative design modelling

Parametric and generative modelling methods are ways in which computer models are made more flexible, and of formalising domain-specific knowledge. At present, no open standard exists for the interchange of parametric and generative information. The Industry Foundation Classes (IFC) which are an open standard for interoperability in building information models is presented as the base for an open standard in parametric modelling. The advantage of allowing parametric and generative representations are that the early design process can allow for more iteration and changes can be implemented quicker than with traditional models. This paper begins with a formal definition of what constitutes to be parametric and generative modelling methods and then proceeds to describe an open standard in which the interchange of components could be implemented. As an illustrative example of generative design, Frazer’s ‘Reptiles’ project from 1968 is reinterpreted.

Assessment as a Generative Dance : Connecting Teaching, Learning and Curriculum

This chapter focuses on learning and assessment as social and cultural practices situated within national and international policy contexts of educational change. Classroom assessment was researched using a conceptualization of knowing in action, or the ‘generative dance’. Fine-grained analyses of interactivity between students, and between teacher and student/s, and their patterns of participation in assessment and learning were conducted. The findings offer original insights into how learners draw on explicit and tacit forms of knowing in order to successfully participate in learning. Assessment is re-imagined as a dynamic space in which teachers learn about their students as they learn with their students, and where all students can be empowered to find success.

Soundage : a practice-led approach to Gertrude Stein, sound, and generative language

In this practice-led research project I work to show how a re-reading and a particular form of listening to the sound-riddled nature of Gertrude Stein's work, Two: Gertrude Stein and her Brother, presents us with a contemporary theory of sound in language. This theory, though in its infancy, is a particular enjambment of sounded language that presents itself as an event, engaged with meaning, with its own inherent voice. It displays a propensity through engagement with the 'other' to erupt into love. In this thesis these qualities are reverberated further through the work of Seth Kim-Cohen's notion of the non-cochlear, Simon Jarvis's notion of musical thinking, Jean-Jacques Lecercle's notion of délire or nonsense, Luce Irigaray's notion of jouissant love and the Bracha Ettinger's notion of the generative matrixial border space. This reading then is simultaneously paired with my own work of scoring and creating a digital opera from Stein's work, thereby testing and performing Stein's theory. In this I show how a re-reading and relistening to Stein's work can be significant to feminist ethical language frames, contemporary philosophy, sonic art theory and digital language frames. Further significance of this study is that when the reverberation of Stein's engagements with language through sound can be listened to, a pattern emerges, one that encouragingly problematizes subjectivity and interweaves genres/methods and means, creating a new frame for sound in language, one with its own voice that I call soundage.

Leistungssteigerung durch das Vergaster-Diesel-Verfahren, mit-Alkohol

Abstract is not available.

NETGEM: Network Embedded Temporal GEnerative Model for gene expression data

Background: Temporal analysis of gene expression data has been limited to identifying genes whose expression varies with time and/or correlation between genes that have similar temporal profiles. Often, the methods do not consider the underlying network constraints that connect the genes. It is becoming increasingly evident that interactions change substantially with time. Thus far, there is no systematic method to relate the temporal changes in gene expression to the dynamics of interactions between them. Information on interaction dynamics would open up possibilities for discovering new mechanisms of regulation by providing valuable insight into identifying time-sensitive interactions as well as permit studies on the effect of a genetic perturbation. Results: We present NETGEM, a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. The model treats the interaction strengths as random variables which are modulated by suitable priors. This approach is necessitated by the extremely small sample size of the datasets, relative to the number of interactions. The model is amenable to a linear time algorithm for efficient inference. Using temporal gene expression data, NETGEM was successful in identifying (i) temporal interactions and determining their strength, (ii) functional categories of the actively interacting partners and (iii) dynamics of interactions in perturbed networks. Conclusions: NETGEM represents an optimal trade-off between model complexity and data requirement. It was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks. Given that the inputs to NETGEM are only the network and the temporal variation of the nodes, this algorithm promises to have widespread applications, beyond biological systems. The source code for NETGEM is available from https://github.com/vjethava/NETGEM

Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a `maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS(GM)), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.