Penalized Regression with Correlation Based Penalty

A new regularization method for regression models is proposed. The criterion to be minimized contains a penalty term which explicitly links strength of penalization to the correlation between predictors. As the elastic net, the method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. A boosted version of the penalized estimator, which is based on a new boosting method, allows to select variables. Real world data and simulations show that the method compares well to competing regularization techniques. In settings where the number of predictors is smaller than the number of observations it frequently performs better than competitors, in high dimensional settings prediction measures favor the elastic net while accuracy of estimation and stability of variable selection favors the newly proposed method

Collapse models: from theoretical foundations to experimental verifications

The basic strategy underlying models of spontaneous wave function collapse (collapse models) is to modify the Schroedinger equation by including nonlinear stochastic terms, which tend to localize wave functions in space in a dynamical manner. These terms have negligible effects on microscopic systems-therefore their quantum behaviour is practically preserved. On the other end, since the strength of these new terms scales with the mass of the system, they become dominant at the macroscopic level, making sure that wave functions of macro-objects are always well-localized in space. We will review these basic features. By changing the dynamics of quantum systems, collapse models make predictions, which are different from standard quantum mechanical predictions. Although they are difficult to detect, we discuss the most relevant scenarios, where such deviations can be observedComment: 10 Pages. Invited Talk at the Heinz von Foerster Centenary International Conference on Self-Organization and Emergence: Emergent Quantum Mechanics (EmerQuM11). Nov. 10-13, 2011, Vienna, Austria. Proceedings to appear in J. Phys. (Conf. Series

Influence of ultrafiltration membrane characteristics on adsorptive fouling with dextrans

This paper presents a detailed investigation of fouling mechanisms for ultrafiltration membranes with polysaccharides obtained by studying membrane–solute (static adsorption) and membrane–solute–solute interactions (ultrafiltration (UF)). Two polyethersulfone (PES) membranes and one stabilized cellulose (cellulosic) membrane with a nominal cut-off of 10 kg/mol and dextrans with average molar mass (M) of 4, 10 and 15 kg/mol were used. The membranes before and after static adsorption of dextran were characterized by captive bubble contact angle and tangential streaming potential measurements as well as ultrafiltration sieving curves for polyethylene glycols. Significant water flux reductions (4–15%), which also correlated with dextran molar mass, and changes of the other membrane characteristics occurred after static dextran adsorption for the PES membranes. An empirical model to describe the correlation between the relative water flux reduction and the concentration of solute had also been proposed. In contrast, no significant changes could be detected for the cellulosic membrane. Significant membrane–solute interactions had also been confirmed in the ultrafiltration experiments with dextrans where irreversible fouling had been observed for the PES but not for the cellulosic membranes. The results provide fundamental information for a better understanding of fouling by polysaccharides. In particular, it had been confirmed that hydrophilic and neutral dextrans can significantly foul PES membranes via adsorption to the surface of the membrane polymer. On this basis, methods for control of this fouling can be properly developed

The time-dependent Aharonov-Casher effect

In this paper we give a covariant expression for Aharonov-Casher phase. This expression is a combination of the canonical electric field, Aharonov-Casher phase plus a magnetic field phase shift. We use this covariant expression for the Aharonov-Casher phase to investigate the case of a neutral particle with a non-zero magnetic moment moving in the {\it time dependent} electric and magnetic fields of a plane electromagnetic wave background. We focus on the case where the magnetic moment of the particle is oriented so that both the electric and magnetic field lead to non-zero phases, and we look at the interplay between these electric and magnetic phases.Comment: 14 pages revtex4, 1 figure, to be published PL

Boosting Correlation Based Penalization in Generalized Linear Models

In high dimensional regression problems penalization techniques are a useful tool for estimation and variable selection. We propose a novel penalization technique that aims at the grouping effect which encourages strongly correlated predictors to be in or out of the model together. The proposed penalty uses the correlation between predictors explicitly. We consider a simple version that does not select variables and a boosted version which is able to reduce the number of variables in the model. Both methods are derived within the framework of generalized linear models. The performance is evaluated by simulations and by use of real world data sets

Combining Quadratic Penalization and Variable Selection via Forward Boosting

Quadratic penalties can be used to incorporate external knowledge about the association structure among regressors. Unfortunately, they do not enforce single estimated regression coefficients to equal zero. In this paper we propose a new approach to combine quadratic penalization and variable selection within the framework of generalized linear models. The new method is called Forward Boosting and is related to componentwise boosting techniques. We demonstrate in simulation studies and a real-world data example that the new approach competes well with existing alternatives especially when the focus is on interpretable structuring of predictors