Approximate Bayesian Computation for astronomical model analysis : a case study in galaxy demographics and morphological transformation at high redshift

Approximate Bayesian Computation’ (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arbitrary set of input parameters may be entirely intractable – the latter condition rendering useless the standard machinery of tractable likelihood-based, Bayesian statistical inference [e.g. conventional Markov chain Monte Carlo (MCMC) simulation]. In this paper, we demonstrate the potential of ABC for astronomical model analysis by application to a case study in the morphological transformation of high-redshift galaxies. To this end, we develop, first, a stochastic model for the competing processes of merging and secular evolution in the early Universe, and secondly, through an ABC-based comparison against the observed demographics of massive (Mgal > 1011 M⊙) galaxies (at 1.5 < z < 3) in the Cosmic Assembly Near-IR Deep Extragalatic Legacy Survey (CANDELS)/Extended Groth Strip (EGS) data set we derive posterior probability densities for the key parameters of this model. The ‘Sequential Monte Carlo’ implementation of ABC exhibited herein, featuring both a self-generating target sequence and self-refining MCMC kernel, is amongst the most efficient of contemporary approaches to this important statistical algorithm. We highlight as well through our chosen case study the value of careful summary statistic selection, and demonstrate two modern strategies for assessment and optimization in this regard. Ultimately, our ABC analysis of the high-redshift morphological mix returns tight constraints on the evolving merger rate in the early Universe and favours major merging (with disc survival or rapid reformation) over secular evolution as the mechanism most responsible for building up the first generation of bulges in early-type discs.

Homing by path integration when a locomotion trajectory crosses itself

Path integration is a process with which navigators derive their current position and orientation by integrating self-motion signals along a locomotion trajectory. It has been suggested that path integration becomes disproportionately erroneous when the trajectory crosses itself. However, there is a possibility that this previous finding was confounded by effects of the length of a traveled path and the amount of turns experienced along the path, two factors that are known to affect path integration performance. The present study was designed to investigate whether the crossover of a locomotion trajectory truly increases errors of path integration. In an experiment, blindfolded human navigators were guided along four paths that varied in their lengths and turns, and attempted to walk directly back to the beginning of the paths. Only one of the four paths contained a crossover. Results showed that errors yielded from the path containing the crossover were not always larger than those observed in other paths, and the errors were attributed solely to the effects of longer path lengths or greater degrees of turns. These results demonstrated that path crossover does not always cause significant disruption in path integration processes. Implications of the present findings for models of path integration are discussed.

The waves in business transformation management: a conceptualization of how IT-enabled Business Transformations Unfold

We examine IT-enabled Business Transformations (ITBT) based on three case studies of successful, multi-year ERP implementation programs. Given the inconsistencies in segmenting the different key periods in ITBTs in both literature and our cases, we sought to consolidate the common events or critical incidents in such initiatives. We label those key periods as waves, and the emergence of triggers and reactions thereunto in the management of business transformations. We show that business transformations unfold in four distinct waves: Wave 1 Concept Development, Wave 2 Blueprint Design, Wave 3 Solution Delivery and Wave 4 Post-Transformation. These waves are characterized by the occurrence of strategic- and program-level triggers to which organizations respond by invoking different management services. Our interpretive research provides a new conceptualization of ITBTs based on a service-oriented view of such initiatives. This view draws attention to managerial capabilities as a service to transformations, and how and when these capabilities are required to respond to triggering incidents. We outline propositions and recommendations for business transformation management.

Processes of transformation : art into pop and back again

This research project was a case study for managing and innovating an interdisciplinary practice: specifically across music, performance and contemporary art. Key works included painting/sound/video installation, experimental performance, electronic pop music, music video and electronic pop music performance. An idiosyncratic and transformative use of colour emerged as an underlying theme and strategy for cohesion. The project offers strategies for the challenges of interdisciplinary practice specifically addressing the limitations related to institutionalised value systems, aesthetic traditions and disciplinary languages.

Creative arts research : a long path to acceptance

The majority of tertiary practice-led creative arts disciplines became part of the Australian university system as a result of the creation of the Unified National System of tertiary education in 1988. Over the past two decades, research has grown as the yardstick by which academic performance in the Australian university sector is recognised and rewarded. Academics in artistic disciplines, who struggled to adapt to a culture and workload expectations different from their previous, predominantly teaching based, employment, continue to see their research under-valued within the established evaluation framework. Despite a late 1990s Australian government funded inquiry, many of the inequities remain. While the Excellence in Research in Australia (ERA) exercise has acknowledged the non-text outputs of artist-academics in its evaluation of 'research outcomes', much of the process remains resolutely framed by measures that work against creative arts researchers.

System dynamics model of the knowledge management process

To effectively manage the challenges being faced by construction organisations in a fast changing business environment, many organisations are attempting to integrate knowledge management (KM) into their business operations. KM activities interact with each other and form a process which receives input from its internal business environment and produces outputs that should be justified by its business performance. This paper aims to provide further understanding on the dynamic nature of the KM process. Through a combination of path analysis and system dynamic simulation, this study found that: 1) an improved business performance enables active KM activities and provide feedback and guidance for formulating learning-based policies; and 2) effective human resource recruitment policies can enlarge the pool of individual knowledge, which lead to a more conducive internal business environment, as well as a higher KM activity level. Consequently, the desired business performance level can be reached within a shorter time frame.

Business transformation management : composition and orchestration of management services

This research develops a better understanding on how large-scale and complex IT-enabled business transformations are managed. Evidence from three global case studies suggest that business transformations can be composed and orchestrated like a jazz band, where improvisation plays a fundamental role to maintain the melody, harmony and rhythm of such initiatives. The thesis details how the jazz metaphor can assist senior management on how to reuse and reconfigure capabilities as services for transforming organizations. To the academic body of knowledge, the thesis provides a study on the use of management services as a theoretical lens to examine Business Transformation Management.

Identifying weak linear features with the "coalescing shortest path image transform"

The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the "Coalescing Shortest Path Image Transform" with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features. © Copyright Microscopy Society of America 2011.

Tubule detection in testis images using boundary weighting and circular shortest path

In studies of germ cell transplantation, measureing tubule diameters and counting cells from different populations using antibodies as markers are very important. Manual measurement of tubule sizes and cell counts is a tedious and sanity grinding work. In this paper, we propose a new boundary weighting based tubule detection method. We first enhance the linear features of the input image and detect the approximate centers of tubules. Next, a boundary weighting transform is applied to the polar transformed image of each tubule region and a circular shortest path is used for the boundary detection. Then, ellipse fitting is carried out for tubule selection and measurement. The algorithm has been tested on a dataset consisting of 20 images, each having about 20 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually. © 2013 IEEE.

Convergence-guaranteed time-varying RRT path planning for profiling floats in 4-Dimensional flow

This paper presents an extension to the Rapidly-exploring Random Tree (RRT) algorithm applied to autonomous, drifting underwater vehicles. The proposed algorithm is able to plan paths that guarantee convergence in the presence of time-varying ocean dynamics. The method utilizes 4-Dimensional, ocean model prediction data as an evolving basis for expanding the tree from the start location to the goal. The performance of the proposed method is validated through Monte-Carlo simulations. Results illustrate the importance of the temporal variance in path execution, and demonstrate the convergence guarantee of the proposed methods.

Practical path planning and obstacle avoidance for autonomous mowing

There is a need for systems which can autonomously perform coverage tasks on large outdoor areas. Unfortunately, the state-of-the-art is to use GPS based localization, which is not suitable for precise operations near trees and other obstructions. In this paper we present a robotic platform for autonomous coverage tasks. The system architecture integrates laser based localization and mapping using the Atlas Framework with Rapidly-Exploring Random Trees path planning and Virtual Force Field obstacle avoidance. We demonstrate the performance of the system in simulation as well as with real world experiments.