Regularized Nonparametric Volterra Kernel Estimation

In this paper, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modelled as Volterra series. The kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realizations of multidimensional Gaussian processes. Based on this, prior information about the structure of the Volterra kernel is introduced via an appropriate penalization term in the least squares cost function. It is shown that the proposed method is able to deliver accurate estimates of the Volterra kernels even in the case of a small amount of data points

Functional selectivity of EM-2 analogs at the mu-opioid receptor

The mu opioid receptor agonists are the most efficacious pain controlling agents but their use is accompanied by severe side effects. More recent developments indicate that some ligands can differentially activate receptor downstream pathways, possibly allowing for dissociation of analgesia mediated through the G protein from the opioid-related side effects mediated by β-arrestin pathway. In an effort to identify such biased ligands, here we present a series of thirteen endomorphin-2 (EM-2) analogs with modifications in positions 1, 2, and/or 3. All obtained analogs behaved as mu receptor selective agonists in calcium mobilization assay carried out on cells expressing opioid receptors and chimeric G proteins. A Bioluminescence Resonance Energy Transfer (BRET) approach was employed to determine the ability of analogs to promote the interaction of the mu opioid receptor with G protein or β-arrestin 2. Nearly half of the developed analogs showed strong bias towards G protein, in addition four compounds were nearly inactive towards β-arrestin 2 recruitment while blocking the propensity of EM-2 to evoke mu-β-arrestin 2 interaction. The data presented here contribute to our understanding of EM-2 interaction with the mu opioid receptor and of the transductional propagation of the signal. In addition, the generation of potent and selective mu receptor agonists strongly biased towards G protein provides the scientific community with novel tools to investigate the in vivo consequences of biased agonism at this receptor

L'applicazione delle "Linee guida" del progetto europeo COME-IN! Cooperazione per una piena accessibilit\ue0 ai musei - verso una maggiore inclusione. L'esempio del Museo Archeologico di Udine

Dal 2014 il Museo Archeologico di Udine ha iniziato un percorso orientato all’accessibilità della struttura museale, degli allestimenti e delle iniziative culturali organizzate, che è approdato, grazie ai finanziamenti del progetto europeo COME-IN! ad una nuova fase di rivisitazione in termini di accessibilità e inclusività. Questo processo ha riguardato non solo l’allestimento del Museo Archeologico, inaugurato nel 2013, ma ha interessato l’intera struttura e in generale i servizi forniti dai Civici Musei di Udine. L’obiettivo di inserire in maniera armoniosa ed equilibrata tutti gli interventi previsti da progetto si è concretizzato nel coinvolgimento di professionisti e associazioni che hanno collaborato in base alle proprie competenz

SVMs for system identification: the linear case

In this work we deal with the application of Support Vector Machines for Regression (SVRs) to the problem of identifying linear dynamic systems on the basis of a set of Input/Output samples. Three different examples of simple linear systems will be considered, taking into account both non-recursive and recursive models. When defining the SVR estimating function, several examples of kernels will be employed, with emphasis on the ones that may be more appropriate for describing linear models. As a further step, the exact parameters of the model will be directly estimated from the Support Vector values resulting from the SVR training phase

Tuning the hyperparameters of the filter-based regularization method for impulse response estimation

This paper discusses the use of a filter-based method for regularized impulse response modeling for linear time-invariant systems. The proposed method is a reformulation of the Bayesian, kernel based impulse response modeling approaches. The filter interpretation of the regularization cost function allows one to develop an intuitive framework to model a wide range of systems with different properties in a flexible way. Two hyperparameter selection techniques, based on Cross Validation and on Marginal Likelihood Maximization are presented. The proposed methods are tested on Monte Carlo simulation examples and on a real robotics problem. The results are compared with the ones obtained with the kernel-based methods based on the DC and TC kernels

Filter-based regularisation for impulse response modelling

\u3cp\u3eIn the last years, the success of kernel-based regularisation techniques in solving impulse response modelling tasks has revived the interest on linear system identification. In this work, an alternative perspective on the same problem is introduced. Instead of relying on a Bayesian framework to include assumptions about the system in the definition of the covariance matrix of the parameters, here the prior knowledge is injected at the cost function level. The key idea is to define the regularisation matrix as a filtering operation on the parameters, which allows for a more intuitive formulation of the problem from an engineering point of view. Moreover, this results in a unified framework to model low-pass, band-pass and high-pass systems, and systems with one or more resonances. The proposed filter-based approach outperforms the existing regularisation method based on the TC and DC kernels, as illustrated by means of Monte Carlo simulations on several linear modelling examples.\u3c/p\u3

Study of the effective number of parameters in nonlinear identification benchmarks

\u3cp\u3eThis paper discusses the importance of the notion of effective number of parameters as a measure of model complexity. Exploiting this concept allows a fair comparison of models obtained from different model classes. Several illustrative examples of linear and nonlinear models are presented to provide more insight in the problem. As one possible way of showing that model complexity can be reduced without having to pull any parameters to zero, an approach for rank reduced estimation based on the truncated SVD is also discussed. These ideas are then applied to two nonlinear real world problems: The Wiener-Hammerstein and the Silverbox benchmarks.\u3c/p\u3