ALICE potential for heavy-flavour physics

The Large Hadron Collider (LHC), where lead nuclei will collide at the unprecedented c.m.s. energy of 5.5 TeV per nucleon-nucleon pair, will offer new and unique opportunities for the study of the properties of strongly interacting matter at high energy density over extended volumes. We will briefly explain why heavy-flavour particles are well-suited tools for such a study and we will describe how the ALICE experiment is preparing to make use of these tools.Comment: 6 pages, 3 figures, prepared for the Proceedings of "Strange Quark Matter 2007", Levoca, Slovaki

A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification

Convolutional Neural Networks (CNNs) have demonstrated their superiority in image classification, and evolutionary computation (EC) methods have recently been surging to automatically design the architectures of CNNs to save the tedious work of manually designing CNNs. In this paper, a new hybrid differential evolution (DE) algorithm with a newly added crossover operator is proposed to evolve the architectures of CNNs of any lengths, which is named DECNN. There are three new ideas in the proposed DECNN method. Firstly, an existing effective encoding scheme is refined to cater for variable-length CNN architectures; Secondly, the new mutation and crossover operators are developed for variable-length DE to optimise the hyperparameters of CNNs; Finally, the new second crossover is introduced to evolve the depth of the CNN architectures. The proposed algorithm is tested on six widely-used benchmark datasets and the results are compared to 12 state-of-the-art methods, which shows the proposed method is vigorously competitive to the state-of-the-art algorithms. Furthermore, the proposed method is also compared with a method using particle swarm optimisation with a similar encoding strategy named IPPSO, and the proposed DECNN outperforms IPPSO in terms of the accuracy.Comment: Accepted by The Australasian Joint Conference on Artificial Intelligence 201

Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph

A recently proposed methodology called the Horizontal Visibility Graph (HVG) [Luque {\it et al.}, Phys. Rev. E., 80, 046103 (2009)] that constitutes a geometrical simplification of the well known Visibility Graph algorithm [Lacasa {\it et al.\/}, Proc. Natl. Sci. U.S.A. 105, 4972 (2008)], has been used to study the distinction between deterministic and stochastic components in time series [L. Lacasa and R. Toral, Phys. Rev. E., 82, 036120 (2010)]. Specifically, the authors propose that the node degree distribution of these processes follows an exponential functional of the form $P(\kappa)\sim \exp(-\lambda~\kappa)$, in which $\kappa$ is the node degree and $\lambda$ is a positive parameter able to distinguish between deterministic (chaotic) and stochastic (uncorrelated and correlated) dynamics. In this work, we investigate the characteristics of the node degree distributions constructed by using HVG, for time series corresponding to $28$ chaotic maps and $3$ different stochastic processes. We thoroughly study the methodology proposed by Lacasa and Toral finding several cases for which their hypothesis is not valid. We propose a methodology that uses the HVG together with Information Theory quantifiers. An extensive and careful analysis of the node degree distributions obtained by applying HVG allow us to conclude that the Fisher-Shannon information plane is a remarkable tool able to graphically represent the different nature, deterministic or stochastic, of the systems under study.Comment: Submitted to PLOS On

Infusing Creativity and Design into a University Faculty Mentor Process: Means and Ends

“So you have a design degree, why are you interested in the area of curriculum and instructional technology?” For me I see so many connections and important contributions to both design and education, in addition to the valuable lessons learned by taking an interdisciplinary approach to projects. This case study provides one example of how design and education, together, can produce exciting processes and results that help inform both design and education scholars