Predicting cation ordering in MgAl2O4 using genetic algorithms and density functional theory

Genetic algorithms (GAs) together with classical pair potentials and density functional theory (DFT) are used to investigate cation order in MgAl₂O₄ (Spinel). To efficiently locate the global minimum/minima on the system potential energy surface, corresponding to the ordered and fully equilibrated low-temperature phase, local structural optimizations are essential. Such energy minimizations are expensive at the DFT level, but a comparison of the distribution of the energy minima from DFT and popular classical pair potentials allows one to rapidly tune the GA parameters. We show that GAs are able to find, not only the global minimum on the potential energy, but also other low-energy cation configurations representing possible frozen-in disordered or metastable phases after quenching. The nature of these low-energy configurations can help to interpret the extent of kinetic trapping which hampers the comparison between different experimental studies.     

Preface

Preface

Preface     

Attribute selection with fuzzy decision reducts

Rough set theory provides a methodology for data analysis based on the approximation of concepts in information systems. It revolves around the notion of discernibility: the ability to distinguish between objects, based on their attribute values. It allows to infer data dependencies that are useful in the fields of feature selection and decision model construction. In many cases, however, it is more natural, and more effective, to consider a gradual notion of discernibility. Therefore, within the context of fuzzy rough set theory, we present a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations. The paper unifies existing work in this direction, and introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure. Experimental results demonstrate the potential of fuzzy decision reducts to discover shorter attribute subsets, leading to decision models with a better coverage and with comparable, or even higher accuracy.     

BirdVis: visualizing and understanding bird populations

Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatialtemporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.     

Community discovery using nonnegative matrix factorization

Complex networks exist in a wide range of real world systems, such as social networks, technological networks, and biological networks. During the last decades, many researchers have concentrated on exploring some common things contained in those large networks include the small-world property, power-law degree distributions, and network connectivity. In this paper, we will investigate another important issue, community discovery, in network analysis. We choose Nonnegative Matrix Factorization (NMF) as our tool to find the communities because of its powerful interpretability and close relationship between clustering methods. Targeting different types of networks (undirected, directed and compound), we propose three NMF techniques (Symmetric NMF, Asymmetric NMF and Joint NMF). The correctness and convergence properties of those algorithms are also studied. Finally the experiments on real world networks are presented to show the effectiveness of the proposed methods.     

Experimental test of a robust formation controller for marine unmanned surface vessels

Experiments with two formation controllers for marine unmanned surface vessels are reported. The formation controllers are designed using the nonlinear robust model-based sliding mode approach. The marine vehicles can operate in arbitrary formation configurations by using two leader-follower control schemes. For the design of these controller schemes 3 degrees of freedom (DOFs) of surge, sway, and yaw are assumed in the planar motion of the marine surface vessels. Each vessel only has two actuators; therefore, the vessels are underactuated and the lack of a kinematic constraint puts them into the holonomic system category. In this work, the position of a control point on the vessel is controlled, and the orientation dynamics is not directly controlled. Therefore, there is a potential for an oscillatory yaw motion to occur. It is shown that the orientation dynamics, as the internal dynamics of this underactuated system, is stable, i.e., the follower vehicle does not oscillate about its control point during the formation maneuvers. The proposed formation controller relies only on the state information obtained from the immediate neighbors of the vessel and the vessel itself. The effectiveness and robustness of formation control laws in the presence of parameter uncertainty and environmental disturbances are demonstrated by using both simulations and field experiments. The experiments were performed in a natural environment on a lake using a small test boat, and show robust performance to parameter uncertainty and disturbance. This paper reports the first experimental verification of the above mentioned approach, whose unique features are the use of a control point, the zero-dynamic stability analysis, the use of leader-follower method and a nonlinear robust control approach.     

Monte Carlo techniques for phrase-based translation

Recent advances in statistical machine translation have used approximate beam search for NP-complete inference within probabilistic translation models. We present an alternative approach of sampling from the posterior distribution defined by a translation model. We define a novel Gibbs sampler for sampling translations given a source sentence and show that it effectively explores this posterior distribution. In doing so we overcome the limitations of heuristic beam search and obtain theoretically sound solutions to inference problems such as finding the maximum probability translation and minimum risk training and decoding.     

Systems modelling for effective mine water management

Concerns about the difficulties in securing water have led the Australian coal mining industry to seek innovative ways to improve its water management and to adopt novel strategies that will lead to less water being used and more water being reused. Simulation tools are essential to assess current water management performance and to predict the efficiency of potential strategies. As water systems on coal mines are complex and consist of various inter-connected elements, a systems approach was selected, which views mine site water management as a system that obtains water from various sources (surface, groundwater), provides sufficient water of suitable quality to the mining tasks (coal beneficiation, dust suppression, underground operations) and maintains environmental performance. In this paper, the model is described and its calibration is illustrated. The results of applying the model for the comparison of the water balances of 7 coal mines in the northern Bowen Basin (Queensland, Australia) are presented. The model is used to assess the impact of applying specific water management strategies. Results show that a simple systems model is an appropriate tool for assessing site performance, for providing guidance to improve performance through strategic planning, and for guiding adoption of site objectives.