Macrophonics II

Macrophonics II presents new Australian work emerging from the leading edge of performance interface research. The program addresses the emerging dialogue between traditional media and emerging digital media, as well as dialogues across a broad range of musical traditions. Recent technological developments are causing a complete reevaluation of the relationships between media and genres in art, and Macrophonics II presents a cross-section of responses to this situation. Works in the program foreground an approach to performance that integrates sensors with novel performance control devices, and/or examine how machines can be made musical in performance. The program presents works by Australian artists Donna Hewitt, Julian Knowles and Wade Marynowsky, with choreography by Avril Huddy and dance performance by Lizzie and Zaimon Vilmanis. From sensor-based microphones and guitars, through performance a/v, to post-rock dronescapes, movement inspired works and experimental electronica, Macrophonics II provides a broad and engaging survey of new performance approaches in mediatised environments. Initial R&D for the work was supported by a range of institutions internationally, including the Australia Council for the Arts, Arts Queensland, STEIM (Holland) and the Nes Artist Residency (Iceland).

A dynamic Web agent for verifying the security and integrity of a Web site's contents

To harness safe operation of Web-based systems in Web environments, we propose an SSPA (Server-based SHA-1 Page-digest Algorithm) to verify the integrity of Web contents before the server issues an HTTP response to a user request. In addition to standard security measures, our Java implementation of the SSPA, which is called the Dynamic Security Surveillance Agent (DSSA), provides further security in terms of content integrity to Web-based systems. Its function is to prevent the display of Web contents that have been altered through the malicious acts of attackers and intruders on client machines. This is to protect the reputation of organisations from cyber-attacks and to ensure the safe operation of Web systems by dynamically monitoring the integrity of a Web site's content on demand. We discuss our findings in terms of the applicability and practicality of the proposed system. We also discuss its time metrics, specifically in relation to its computational overhead at the Web server, as well as the overall latency from the clients' point of view, using different Internet access methods. The SSPA, our DSSA implementation, some experimental results and related work are all discussed

Multi-scale bio-inspired place recognition

This paper presents a novel place recognition algorithm inspired by the recent discovery of overlapping and multi-scale spatial maps in the rodent brain. We mimic this hierarchical framework by training arrays of Support Vector Machines to recognize places at multiple spatial scales. Place match hypotheses are then cross-validated across all spatial scales, a process which combines the spatial specificity of the finest spatial map with the consensus provided by broader mapping scales. Experiments on three real-world datasets including a large robotics benchmark demonstrate that mapping over multiple scales uniformly improves place recognition performance over a single scale approach without sacrificing localization accuracy. We present analysis that illustrates how matching over multiple scales leads to better place recognition performance and discuss several promising areas for future investigation.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

A high frequency, low voltage stackable full bridge module for modular multilevel cascade converter (MMCC) research

A new small full bridge module for MMCC research is presented. Each full bridge converter cell is a single small (65 × 30 mm) multilayer PCB with two low voltage high current (22 V, 40 A) integrated half bridge ICs and the necessary isolated control signals and auxiliary power supply (2500 V isolation). All devices are surface mount, minimising cell height (4 mm) and parasitic inductance. Each converter cell can be physically stacked with PCB connectors propagating the control signals and inter-cell power connections. Many cells can be trivially stacked to create a large multilevel converter leg with isolated auxiliary power and control signals. Any of the MMCC family members is then easily formed. With a change in placement of stacking connector, a parallel connection of bridges is also possible. Operation of a nine level parallel full bridge is demonstrated at 12 V and 384 kHz switching frequency delivering a 30 W 2 kHz sinewave into a resistive load. A number of new applications for this novel module aside from MMCC development are listed.

An improved gate driver for power MOSFETs using a cascode configuration

To overcome the limitations of existing gate drive topologies an improved gate drive concept is proposed to provide fast, controlled switching of power MOSFETs. The proposed topology exploits the cascode configuration with the inclusion of an active gate clamp to ensure that the driven MOSFET may be turned off under all load conditions. Key operating principles and advantages of the proposed gate drive topology are discussed. Characteristic waveforms are investigated via simulation and experimentation for the cascode driver in an inductive switching application at 375V and 10A. Experimental waveforms compared well with simulations with long gate charging delays (including the Miller plateau) being eliminated from the gate voltage waveform.

Synthesis of orchestrators from service choreographies

Interaction topologies in service-oriented systems are usually classified into two styles: choreographies and orchestrations. In a choreography, services interact in a peer-to-peer manner and no service plays a privileged role. In contrast, interactions in an orchestration occur between one particular service, the orchestrator, and a number of subordinated services. Each of these topologies has its trade-offs. This paper considers the problem of migrating a service-oriented system from a choreography style to an orchestration style. Specifically, the paper presents a tool chain for synthesising orchestrators from choreographies. Choreographies are initially represented as communicating state machines. Based on this representation, an algorithm is presented that synthesises the behaviour of an orchestrator, which is also represented as a state machine. Concurrent regions are then identified in the synthesised state machine to obtain a more compact representation in the form of a Petri net. Finally, it is shown how the resulting Petri nets can be transformed into notations supported by commercial tools, such as the Business Process Modelling Notation (BPMN).

Great expectations : a project by Boxcopy for Federation Square, Next Wave Festival 2008

Part of the Next Wave MEMBRANE Project, Great Expectations draws attention to the parallels between our expectations of art and new technology to make the world a better place. The theme of the 2008 Next Wave Festival, ‘Closer Together’, refers to the way society is ― for the better or for the worse ― becoming increasingly connected by media and communication technologies. Sceptical of the acclaimed social achievements of new technologies, Boxcopy: Contemporary Art Space, a Brisbane-based artist-run initiative, explores the futility of human activities, including art production and consumption, with a collection of works created by young and emerging Brisbane artists. Works for this project include: Early machines such as the Commodore 64 were tape-based, and hence had their games distributed on ordinary cassettes (2009) by Tim Kerr & Extra Features (2008) by Tim Woodward; Spine (2008), Joseph Briekers; Whiteout (2008), Channon Goodwin; Explosive Revelations (2008), Daniel McKewen.

Discovery of an archetypal protein transport system in bacterial outer membranes

Bacteria have mechanisms to export proteins for diverse purposes, including colonization of hosts and pathogenesis. A small number of archetypal bacterial secretion machines have been found in several groups of bacteria and mediate a fundamentally distinct secretion process. Perhaps erroneously, proteins called 'autotransporters' have long been thought to be one of these protein secretion systems. Mounting evidence suggests that autotransporters might be substrates to be secreted, not an autonomous transporter system. We have discovered a new translocation and assembly module (TAM) that promotes efficient secretion of autotransporters in proteobacteria. Functional analysis of the TAM in Citrobacter rodentium, Salmonella enterica and Escherichia coli showed that it consists of an Omp85-family protein, TamA, in the outer membrane and TamB in the inner membrane of diverse bacterial species. The discovery of the TAM provides a new target for the development of therapies to inhibit colonization by bacterial pathogens.

DSB proteins and bacterial pathogenicity

If DNA is the information of life, then proteins are the machines of life — but they must be assembled and correctly folded to function. A key step in the protein-folding pathway is the introduction of disulphide bonds between cysteine residues in a process called oxidative protein folding. Many bacteria use an oxidative protein-folding machinery to assemble proteins that are essential for cell integrity and to produce virulence factors. Although our current knowledge of this machinery stems largely from Escherichia coli K-12, this view must now be adjusted to encompass the wider range of disulphide catalytic systems present in bacteria.

Profiling : an application assignment approach for green data centers

In the past few years, there has been a steady increase in the attention, importance and focus of green initiatives related to data centers. While various energy aware measures have been developed for data centers, the requirement of improving the performance efficiency of application assignment at the same time has yet to be fulfilled. For instance, many energy aware measures applied to data centers maintain a trade-off between energy consumption and Quality of Service (QoS). To address this problem, this paper presents a novel concept of profiling to facilitate offline optimization for a deterministic application assignment to virtual machines. Then, a profile-based model is established for obtaining near-optimal allocations of applications to virtual machines with consideration of three major objectives: energy cost, CPU utilization efficiency and application completion time. From this model, a profile-based and scalable matching algorithm is developed to solve the profile-based model. The assignment efficiency of our algorithm is then compared with that of the Hungarian algorithm, which does not scale well though giving the optimal solution.

A distributed computing framework for all-to-all comparison problems

Distributed computation and storage have been widely used for processing of big data sets. For many big data problems, with the size of data growing rapidly, the distribution of computing tasks and related data can affect the performance of the computing system greatly. In this paper, a distributed computing framework is presented for high performance computing of All-to-All Comparison Problems. A data distribution strategy is embedded in the framework for reduced storage space and balanced computing load. Experiments are conducted to demonstrate the effectiveness of the developed approach. They have shown that about 88% of the ideal performance capacity have be achieved in multiple machines through using the approach presented in this paper.

Models of protein linear molecular motors for dynamic nanodevices

Protein molecular motors are natural nano-machines that convert the chemical energy from the hydrolysis of adenosine triphosphate into mechanical work. These efficient machines are central to many biological processes, including cellular motion, muscle contraction and cell division. The remarkable energetic efficiency of the protein molecular motors coupled with their nano-scale has prompted an increasing number of studies focusing on their integration in hybrid micro- and nanodevices, in particular using linear molecular motors. The translation of these tentative devices into technologically and economically feasible ones requires an engineering, design-orientated approach based on a structured formalism, preferably mathematical. This contribution reviews the present state of the art in the modelling of protein linear molecular motors, as relevant to the future design-orientated development of hybrid dynamic nanodevices. © 2009 The Royal Society of Chemistry.

A high performance liquid chromatography and electrospray ionization mass spectrometry method for the analysis of the natural medicine, Forsythia suspensa

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.