Beyond Markov Chains, Towards Adaptive Memristor Network-based Music Generation

We undertook a study of the use of a memristor network for music generation, making use of the memristor's memory to go beyond the Markov hypothesis. Seed transition matrices are created and populated using memristor equations, and which are shown to generate musical melodies and change in style over time as a result of feedback into the transition matrix. The spiking properties of simple memristor networks are demonstrated and discussed with reference to applications of music making. The limitations of simulating composing memristor networks in von Neumann hardware is discussed and a hardware solution based on physical memristor properties is presented.Comment: 22 pages, 13 pages, conference pape

Ami-The Chemist's Amanuensis

Poster presented at the VSMF Symposium held at the Unilever Centre on 2011-01-17The Ami project was a six month Rapid Innovation project sponsored by JISC to explore the Virtual Research Environment space. The project brainstormed with chemists and decided to investigate ways to facilitate monitoring and collection of experimental data. A frequently encountered use-case was identified of how the chemist reaches the end of an experiment, but finds an unexpected result. The ability to replay events can significantly help make sense of how things progressed. The project therefore concentrated on collecting a variety of dimensions of ancillary data – data that would not normally be collected due to practicality constraints. There were three main areas of investigation: 1) Development of a monitoring tool using infrared and ultrasonic sensors; 2) Time-lapse motion video capture (for example, videoing 5 seconds in every 60); and 3) Activity-driven video monitoring of the fume cupboard environs. The Ami client application was developed to control these separate logging functions. The application builds up a timeline of the events in the experiment and around the fume cupboard. The videos and data logs can then be reviewed after the experiment in order to help the chemist determine the exact timings and conditions used. The project experimented with ways in which a Microsoft Kinect could be used in a laboratory setting. Investigations suggest that it would not be an ideal device for controlling a mouse, but it shows promise for usages such as manipulating virtual molecules.Ami was a six month project under the “JISC Rapid Innovation Grants 10/09” programme, Grants for the Virtual Research Environment - Rapid Innovation funding call. [http://www.jisc.ac.uk/fundingopportunities/funding_calls/2009/10/vreri.aspx

On-demand or Spot? Selling the cloud to risk-averse customers

In Amazon EC2, cloud resources are sold through a combination of an on-demand market, in which customers buy resources at a fixed price, and a spot market, in which customers bid for an uncertain supply of excess resources. Standard market environments suggest that an optimal design uses just one type of market. We show the prevalence of a dual market system can be explained by heterogeneous risk attitudes of customers. In our stylized model, we consider unit demand risk-averse bidders. We show the model admits a unique equilibrium, with higher revenue and higher welfare than using only spot markets. Furthermore, as risk aversion increases, the usage of the on-demand market increases. We conclude that risk attitudes are an important factor in cloud resource allocation and should be incorporated into models of cloud markets.Comment: Appeared at WINE 201

Audio-to-Visual Speech Conversion using Deep Neural Networks

We study the problem of mapping from acoustic to visual speech with the goal of generating accurate, perceptually natural speech animation automatically from an audio speech signal. We present a sliding window deep neural network that learns a mapping from a window of acoustic features to a window of visual features from a large audio-visual speech dataset. Overlapping visual predictions are averaged to generate continuous, smoothly varying speech animation. We outperform a baseline HMM inversion approach in both objective and subjective evaluations and perform a thorough analysis of our results

Using sorbent waste materials to enhance treatment of micro-point source effluents by constructed wetlands

Sorbent materials are widely used in environmental settings as a means of = enhancing pollution remediation. A key area of environmental concern is that of water pollution, including the need to treat micro-point sources of wastewater pollution, such as from caravan sites or visitor centres. Constructed wetlands (CWs) represent one means for effective treatment of wastewater from small wastewater producers, in part because they are believed to be economically viable and environmentally sustainable. Constructed wetlands have the potential to remove a range of pollutants found in wastewater, including nitrogen (N), phosphorus (P), biochemical oxygen demand (BOD) and carbon (C), whilst also reducing the total suspended solids (TSS) concentration in effluents. However, there remain particular challenges for P and N removal from wastewater in CWs, as well as the sometimes limited BOD removal within these treatment systems, particularly for micro-point sources of wastewater. It has been hypothesised that the amendment of CWs with sorbent materials can enhance their potential to treat wastewater, particularly through enhancing the removal of N and P. This paper focuses on data from batch and mesocosm studies that were conducted to identify and assess sorbent materials suitable for use within CWs. The aim in using sorbent material was to enhance the combined removal of phosphate (PO4-P) and ammonium (NH4-N). The key selection criteria for the sorbent materials were that they possess effective PO4-P, NH4-N or combined pollutant removal, come from low cost and sustainable sources, have potential for reuse, for example as a fertiliser or soil conditioner, and show limited potential for re-release of adsorbed nutrients. The sorbent materials selected for testing were alum sludge from water treatment works, ochre derived from minewater treatment, biochar derived from various feedstocks, plasterboard and zeolite. The performance of the individual sorbents was assessed through preliminary desorption studies, isotherm and kinetic adsorption studies, as well as through final desorption studies. Batch studies demonstrated that alum sludge and ochre effectively removed PO4-P from solution (maximum sorption capacity up to 45 mg/g), whilst biochar from both bamboo and rice feedstocks demonstrated effective removal of NH4-N from solution. The potential benefit of using combined reactive media in conjunction with wastewater recirculation to enhance N, P and C treatment was examined using mesocosm studies, and we report initial data from these mesocosm studies

Experimental testing and predictive machine learning to determine the mechanical characteristics of corroded reinforcing steel

Chloride-induced deterioration of reinforcing steel bars has become a densely researched topic over the past several decades because of the severe ramifications to the structural reliability of aging infrastructure. The ever-growing volume of experimental and field data continually enables advances in the field through deeper micro-macro analyses and various modeling applications. The purpose of this paper is twofold. First, an experimental program is introduced, describing the tensile testing of 284 artificially corroded, 25 mm diameter deformed Grade500E reinforcing bars. Secondly, the mechanical characteristics of corroded bars are predicted through a collection of regression-based machine learning algorithms. Models are trained and tested on a database of 1387 tensile tests compiled from 25 other experimental programs available in the literature. The complete database includes 19 input parameters used to predict nine key mechanical properties of the corroded steel bars. Nine machine learning models were selected from a balanced assortment of algorithm typologies to determine the most appropriate methodology for each response variable. The adaptive-neuro fuzzy inference system (ANFIS) model was found to have the strongest individual predictive ability across all models. Meanwhile, ensemble tree-based learning algorithms categorically provided the most consistently high-performing models over the selected response variables