Robust nonlinear system identification: Bayesian mixture of experts using the t-distribution

A novel variational Bayesian mixture of experts model for robust regression of bifurcating and piece-wise continuous processes is introduced. The mixture of experts model is a powerful model which probabilistically splits the input space allowing different models to operate in the separate regions. However, current methods have no fail-safe against outliers. In this paper, a robust mixture of experts model is proposed which consists of Student-t mixture models at the gates and Student-t distributed experts, trained via Bayesian inference. The Student-t distribution has heavier tails than the Gaussian distribution, and so it is more robust to outliers, noise and nonnormality in the data. Using both simulated data and real data obtained from the Z24 bridge this robust mixture of experts performs better than its Gaussian counterpart when outliers are present. In particular, it provides robustness to outliers in two forms: unbiased parameter regression models, and robustness to overfitting/complex models

Highlights of the SLD Physics Program at the SLAC Linear Collider

Starting in 1989, and continuing through the 1990s, high-energy physics witnessed a flowering of precision measurements in general and tests of the standard model in particular, led by e+e- collider experiments operating at the Z0 resonance. Key contributions to this work came from the SLD collaboration at the SLAC Linear Collider. By exploiting the unique capabilities of this pioneering accelerator and the SLD detector, including a polarized electron beam, exceptionally small beam dimensions, and a CCD pixel vertex detector, SLD produced a broad array of electroweak, heavy-flavor, and QCD measurements. Many of these results are one of a kind or represent the world's standard in precision. This article reviews the highlights of the SLD physics program, with an eye toward associated advances in experimental technique, and the contribution of these measurements to our dramatically improved present understanding of the standard model and its possible extensions.Comment: To appear in 2001 Annual Review of Nuclear and Particle Science; 78 pages, 31 figures; A version with higher resolution figures can be seen at http://www.slac.stanford.edu/pubs/slacpubs/8000/slac-pub-8985.html; Second version incorporates minor changes to the tex

Some Recent Developments in SHM Based on Nonstationary Time Series Analysis

Many of the algorithms used for structural health monitoring (SHM) are based on, or motivated by, time series analysis. Quite often, detection methods are variants of approaches developed within the statistical process control (SPC) community. Many of the algorithms used represent mature theory and have a rigorous probabilistic or mathematical basis. However, one of the main issues facing SHM practitioners is that the structures of interest rarely respect the assumptions inherent in deriving algorithms. In the case of time series data, SPC-based approaches usually require the data to be stationary and, unfortunately, SHM data are often nonstationary because of benign variations in the environment of the structure of interest, or because of deliberate operational changes in the use of the structure. This nonstationarity can manifest itself as slowly varying trends on the data or in abrupt switches between regimes. Recent work in nonstationary time series methods for SHM has made considerable progress in accommodating nonstationarity and some of that work is discussed within this paper: in terms of understanding slowly varying trends, the cointegration algorithm from econometrics is presented; for understanding abrupt switches, Bayesian mixtures of experts are presented. Another issue in time series analysis is indirectly related to the assumption of linear behavior of structures and the impact of this assumption is briefly considered in terms of its effects on detection thresholds in SPC-like methods; again, progress has been made recently. Some issues still remain, and these are discussed also

Suppression of planar cell polarity signaling and migration in glioblastoma by Nrdp1-mediated Dvl polyubiquitination.

The lethality of the aggressive brain tumor glioblastoma multiforme (GBM) results in part from its strong propensity to invade surrounding normal brain tissue. Although oncogenic drivers such as epidermal growth factor receptor activation and Phosphatase and Tensin homolog inactivation are thought to promote the motility and invasiveness of GBM cells via phosphatidylinostitol 3-kinase activation, other unexplored mechanisms may also contribute to malignancy. Here we demonstrate that several components of the planar cell polarity (PCP) arm of non-canonical Wnt signaling including VANGL1, VANGL2 and FZD7 are transcriptionally upregulated in glioma and correlate with poorer patient outcome. Knockdown of the core PCP pathway component VANGL1 suppresses the motility of GBM cell lines, pointing to an important mechanistic role for this pathway in glioblastoma malignancy. We further observe that restoration of Nrdp1, a RING finger type E3 ubiquitin ligase whose suppression in GBM also correlates with poor prognosis, reduces GBM cell migration and invasiveness by suppressing PCP signaling. Our observations indicate that Nrdp1 physically interacts with the Vangl1 and Vangl2 proteins to mediate the K63-linked polyubiquitination of the Dishevelled, Egl-10 and Pleckstrin (DEP) domain of the Wnt pathway protein Dishevelled (Dvl). Ubiquitination hinders Dvl binding to phosphatidic acid, an interaction necessary for efficient Dvl recruitment to the plasma membrane upon Wnt stimulation of Fzd receptor and for the propagation of downstream signals. We conclude that the PCP pathway contributes significantly to the motility and hence the invasiveness of GBM cells, and that Nrdp1 acts as a negative regulator of PCP signaling by inhibiting Dvl through a novel polyubiquitination mechanism. We propose that the upregulation of core PCP components, together with the loss of the key negative regulator Nrdp1, act coordinately to promote GBM invasiveness and malignancy