A Bayesian network approach to explaining time series with changing structure

Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representa- tion and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset

Photonic band structure of ZnO photonic crystal slab laser

We recently reported on the first realization of ultraviolet photonic crystal laser based on zinc oxide [Appl. Phys. Lett. {\bf 85}, 3657 (2004)]. Here we present the details of structural design and its optimization. We develop a computational super-cell technique, that allows a straightforward calculation of the photonic band structure of ZnO photonic crystal slab on sapphire substrate. We find that despite of small index contrast between the substrate and the photonic layer, the low order eigenmodes have predominantly transverse-electric (TE) or transverse-magnetic (TM) polarization. Because emission from ZnO thin film shows strong TE preference, we are able to limit our consideration to TE bands, spectrum of which can possess a complete photonic band gap with an appropriate choice of structure parameters. We demonstrate that the geometry of the system may be optimized so that a sizable band gap is achieved.Comment: 8 pages, 7 figure

Variable grouping in multivariate time series via correlation

The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the “variable groupings” problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping metho

Long-distant contribution and χc1\chi_{c1} radiative decays to light vector meson

The discrepancy between the PQCD calculation and the CLEO data for $\chi_{c1}\to \gamma V$ ($V=\rho^0,\,\omega,\,\phi$) stimulates our interest in exploring extra mechanism of $\chi_{c1}$ decay. In this work, we apply an important non-perturbative QCD effect, i.e., hadronic loop mechanism, to study $\chi_{c1}\to \gamma V$ radiative decay. Our numerical result shows that the theoretical results including the hadronic loop contribution and the PQCD calculation of $\chi_{c1}\to \gamma V$ are consistent with the corresponding CLEO data of $\chi_{c1}\to \gamma V$. We expect further experimental measurement of $\chi_{c1}\to \gamma V$ at BES-III, which will be helpful to test the hadronic loop effect on $\chi_{c1}$ decay.Comment: 7 pages, 2 figures. Accepted for publication in Eur. Phys. J.

Temporal Bayesian classifiers for modelling muscular dystrophy expression data

The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a new architecture of Bayesian classifier that can be used to understand how biological mechanisms differ with respect to time. We show that this classifier improves the classification of microarray data and at the same time ensures that the models can easily be analysed by biologists by incorporating time transparently. In this paper we focus on data that has been generated to explore different types of muscular dystrophy

The robust selection of predictive genes via a simple classifier

Identifying genes that direct the mechanism of a disease from expression data is extremely useful in understanding how that mechanism works. This in turn may lead to better diagnoses and potentially can lead to a cure for that disease. This task becomes extremely challenging when the data are characterised by only a small number of samples and a high number of dimensions, as it is often the case with gene expression data. Motivated by this challenge, we present a general framework that focuses on simplicity and data perturbation. These are the keys for the robust identification of the most predictive features in such data. Within this framework, we propose a simple selective na¨ıve Bayes classifier discovered using a global search technique, and combine it with data perturbation to increase its robustness to small sample sizes. An extensive validation of the method was carried out using two applied datasets from the field of microarrays and a simulated dataset, all confounded by small sample sizes and high dimensionality. The method has been shown capable of identifying genes previously confirmed or associated with prostate cancer and viral infections