Electroactive micro and nanowells for optofluidic storage

This paper reports an optofluidic architecture which enables reversible trapping, detection and long term storage of spectrally multiplexed semiconductor quantum dot cocktails in electrokinetically active wells ranging in size from 200nm to 5μm. Here we describe the microfluidic delivery of these cocktails, fabrication method and principal of operation for the wells, and characterize the readout capabilities, storage and erasure speeds, internal spatial signal uniformity and potential storage density of the devices. We report storage and erase speeds of less than 153ms and 30ms respectively and the ability to provide 6-bit storage in a single 200nm well through spectral and intensity multiplexing. Furthermore, we present a novel method for enabling passive long term storage of the quantum dots in the wells by transporting them through an agarose gel matrix. We envision that this technique could find eventual application in fluidic memory or display devices

Effects of variations of load distribution on network performance

This paper is concerned with the characterization of the relationship between topology and traffic dynamics. We use a model of network generation that allows the transition from random to scale free networks. Specifically, we consider three different topological types of network: random, scale-free with \gamma = 3, scale-free with \gamma = 2. By using a novel LRD traffic generator, we observe best performance, in terms of transmission rates and delivered packets, in the case of random networks. We show that, even if scale-free networks are characterized by shorter characteristic-path- length (the lower the exponent, the lower the path-length), they show worst performances in terms of communication. We conjecture this could be explained in terms of changes in the load distribution, defined here as the number of shortest paths going through a given vertex. In fact, that distribu- tion is characterized by (i) a decreasing mean (ii) an increas- ing standard deviation, as the networks becomes scale-free (especially scale-free networks with low exponents). The use of a degree-independent server also discriminates against a scale-free structure. As a result, since the model is un- controlled, most packets will go through the same vertices, favoring the onset of congestion.Comment: 4 pages, 4 figures, included in conference proceedings ISCAS 2005, Kobe Japa

Non-gaussianity of the critical 3d Ising model

We discuss the 4pt function of the critical 3d Ising model, extracted from recent conformal bootstrap results. We focus on the non-gaussianity Q - the ratio of the 4pt function to its gaussian part given by three Wick contractions. This ratio reveals significant non-gaussianity of the critical fluctuations. The bootstrap results are consistent with a rigorous inequality due to Lebowitz and Aizenman, which limits Q to lie between 1/3 and 1.Comment: 10 pages, 6 figures; v2: refs added; v3: refs updated, published version; v4: acknowledgement adde

Communication models with distributed transmission rates and buffer sizes

The paper is concerned with the interplay between network structure and traffic dynamics in a communications network, from the viewpoint of end-to-end performance of packet transfer. We use a model of network generation that allows the transition from random to scale-free networks. Specifically, we are able to consider three different topologycal types of networks: (a) random; (b) scale-free with \gamma=3; (c) scale free with \gamma=2. We also use an LRD traffic generator in order to reproduce the fractal behavior that is observed in real world data communication. The issue is addressed of how the traffic behavior on the network is influenced by the variable factors of the transmission rates and queue length restrictions at the network vertices. We show that these factors can induce drastic changes in the throughput and delivery time of network performance and are able to counter-balance some undesirable effects due to the topology.Comment: 4 pages, 5 figures, IEEE Symposium on Circuits and Systems, Island of Kos, Greece, 200

Modelling and Forecasting Dynamic VaR Thresholds for Risk Management and Regulation

The paper presents methods of estimating Value-at-Risk (VaR) thresholds utilising two calibrated models and three conditional volatility or GARCH models. These are used to estimate and forecast the VaR thresholds of an equally-weighted portfolio, comprising: the S & P500, CAC40, FTSE100 a Swiss market index (SMI). On the basis of the number of (non-)violations of the Basel Accord thresholds, the best performing model is PS-GARCH, followed by VARMA-AGARCH, then Portfolio-GARCH and the RiskmetricsTM -EWMA models, both of which would attract a penalty of 0.5. The worst forecasts are obtained from the standard normal method based on historical variances.Value at Risk (VaR) modelling, forecasting risk thresholds, Portfolio Spillover-Garch, risk management and regulation Acknowledgements: The authors wish to thank Felix Chan, Suhejla Hoti, Alex Zsimayer and seminar participants at the Institute of Economics, Academia Sinica, Taiwan, Ling Tung Institute of Technology, Griffith University, Queensland University of Technology, and University of Queensland for helpful comments and suggestions. The first and second authors wish to thank the Australian Research Council for financial support. The third author wishes to acknowledge a University Postgraduate Award and an International Postgraduate Research Scholarship at the University of Western Australia.