Multifractal Fluctuations in Seismic Interspike Series

Multifractal fluctuations in the time dynamics of seismicity data have been analyzed. We investigated the interspike intervals (times between successive earthquakes) of one of the most seismically active areas of central Italy by using the Multifractal Detrended Fluctuation Analysis (MF-DFA). Analyzing the time evolution of the multifractality degree of the series, a loss of multifractality during the aftershocks is revealed. This study aims to suggest another approach to investigate the complex dynamics of earthquakes

Joint Clustering and Registration of Functional Data

Curve registration and clustering are fundamental tools in the analysis of functional data. While several methods have been developed and explored for either task individually, limited work has been done to infer functional clusters and register curves simultaneously. We propose a hierarchical model for joint curve clustering and registration. Our proposal combines a Dirichlet process mixture model for clustering of common shapes, with a reproducing kernel representation of phase variability for registration. We show how inference can be carried out applying standard posterior simulation algorithms and compare our method to several alternatives in both engineered data and a benchmark analysis of the Berkeley growth data. We conclude our investigation with an application to time course gene expression

The Classical Analogue of CP-violation

The phenomenological features of the mixing in the neutral pseudoscalar mesons K0-K0bar can be illustrated in the classical framework of mechanics and by means of electromagnetic coupled circuits. The time-reversed not-invariant processes and the related phenomenon of CP-nonconservation can be induced by dissipative effects which yield a not vanishing imaginary part for the relevant Hamiltonian. Thus, two coupled dissipative oscillators can resemble the peculiar asymmetries which are so common in the realm of high energy particle physics.Comment: 12 pages, Latex, 2 figures available by fa

Bayesian Inference for Latent Biologic Structure with Determinantal Point Processes (DPP)

We discuss the use of the determinantal point process (DPP) as a prior for latent structure in biomedical applications, where inference often centers on the interpretation of latent features as biologically or clinically meaningful structure. Typical examples include mixture models, when the terms of the mixture are meant to represent clinically meaningful subpopulations (of patients, genes, etc.). Another class of examples are feature allocation models. We propose the DPP prior as a repulsive prior on latent mixture components in the first example, and as prior on feature-specific parameters in the second case. We argue that the DPP is in general an attractive prior model for latent structure when biologically relevant interpretation of such structure is desired. We illustrate the advantages of DPP prior in three case studies, including inference in mixture models for magnetic resonance images (MRI) and for protein expression, and a feature allocation model for gene expression using data from The Cancer Genome Atlas. An important part of our argument are efficient and straightforward posterior simulation methods. We implement a variation of reversible jump Markov chain Monte Carlo simulation for inference under the DPP prior, using a density with respect to the unit rate Poisson process

Large-Scale information extraction from textual definitions through deep syntactic and semantic analysis

We present DEFIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DEFIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations

Comparison of Clustering Methods for Time Course Genomic Data: Applications to Aging Effects

Time course microarray data provide insight about dynamic biological processes. While several clustering methods have been proposed for the analysis of these data structures, comparison and selection of appropriate clustering methods are seldom discussed. We compared $3$ probabilistic based clustering methods and $3$ distance based clustering methods for time course microarray data. Among probabilistic methods, we considered: smoothing spline clustering also known as model based functional data analysis (MFDA), functional clustering models for sparsely sampled data (FCM) and model-based clustering (MCLUST). Among distance based methods, we considered: weighted gene co-expression network analysis (WGCNA), clustering with dynamic time warping distance (DTW) and clustering with autocorrelation based distance (ACF). We studied these algorithms in both simulated settings and case study data. Our investigations showed that FCM performed very well when gene curves were short and sparse. DTW and WGCNA performed well when gene curves were medium or long ($>=10$ observations). SSC performed very well when there were clusters of gene curves similar to one another. Overall, ACF performed poorly in these applications. In terms of computation time, FCM, SSC and DTW were considerably slower than MCLUST and WGCNA. WGCNA outperformed MCLUST by generating more accurate and biological meaningful clustering results. WGCNA and MCLUST are the best methods among the 6 methods compared, when performance and computation time are both taken into account. WGCNA outperforms MCLUST, but MCLUST provides model based inference and uncertainty measure of clustering results

Reconstruction of Zeff profiles at TEXTOR through Bayasian source separation

The understanding of the behaviour of impurities is a critical issue in tokamak physics. The ion effective charge Zeff provides a measure for impurity concentration. On the TEXTOR tokamak (Julich, Germany), we run a diagnostic to determine Zeff from the bremsstrahlung emissivity E. From radial profiles of E, electron density ne and temperature Te, profiles for Zeff can be reconstructed. However, their interpretation is difficult outside the plasma centre, because of various uncertainties in E, ne and Te at the edge, which render the radial matching of the different profiles problematic. Conversely, if it were possible to obtain a set of line-integrated values for Zeff directly from the line-integrated measurements of E, ne and Te, then these problems would be avoided. Now, recent advances in the field of statistical signal processing allow the extraction of an unknown signal from a signal mixture. In particular, we describe a procedure for the single-channel Bayesian source separation of a line-integrated Zeff signal from a line-integrated emissivity source, using as a forward model a linearized version of the known functional dependence of Zeff on E, ne and Te. Here, a line-integral over a traditionally obtained Zeff profile may serve as a prior for the line-integrated Zeff signal. In this way, precise information on the electron density and temperature may even become superfluous for the determination of Zeff.Comment: 12th International Congress on Plasma Physics, 25-29 October 2004, Nice (France