A comparative evaluation of nonlinear dynamics methods for time series prediction

A key problem in time series prediction using autoregressive models is to fix the model order, namely the number of past samples required to model the time series adequately. The estimation of the model order using cross-validation may be a long process. In this paper, we investigate alternative methods to cross-validation, based on nonlinear dynamics methods, namely Grassberger-Procaccia, K,gl, Levina-Bickel and False Nearest Neighbors algorithms. The experiments have been performed in two different ways. In the first case, the model order has been used to carry out the prediction, performed by a SVM for regression on three real data time series showing that nonlinear dynamics methods have performances very close to the cross-validation ones. In the second case, we have tested the accuracy of nonlinear dynamics methods in predicting the known model order of synthetic time series. In this case, most of the methods have yielded a correct estimate and when the estimate was not correct, the value was very close to the real one

ODE parameter inference using adaptive gradient matching with Gaussian processes

Parameter inference in mechanistic models based on systems of coupled differential equa- tions is a topical yet computationally chal- lenging problem, due to the need to fol- low each parameter adaptation with a nu- merical integration of the differential equa- tions. Techniques based on gradient match- ing, which aim to minimize the discrepancy between the slope of a data interpolant and the derivatives predicted from the differen- tial equations, offer a computationally ap- pealing shortcut to the inference problem. The present paper discusses a method based on nonparametric Bayesian statistics with Gaussian processes due to Calderhead et al. (2008), and shows how inference in this model can be substantially improved by consistently inferring all parameters from the joint dis- tribution. We demonstrate the efficiency of our adaptive gradient matching technique on three benchmark systems, and perform a de- tailed comparison with the method in Calder- head et al. (2008) and the explicit ODE inte- gration approach, both in terms of parameter inference accuracy and in terms of computa- tional efficiency

Violation of the Wiedemann-Franz law for one-dimensional ultracold atomic gases

We study energy and particle transport for one-dimensional strongly interacting bosons through a ballistic single channel connecting two atomic reservoirs. We show the emergence of particle- and energy-current separation, leading to the violation of the Wiedemann-Franz law. As a consequence, we predict different time scales for the equilibration of temperature and particle imbalances between the reservoirs. Going beyond the linear spectrum approximation, we show the emergence of thermoelectric effects, which could be controlled by either tuning interactions or the temperature. Our results describe, in a unified picture, fermions in condensed-matter devices and bosons in ultracold atom setups. We conclude by discussing the effects of a controllable disorder

Parameter inference in mechanistic models of cellular regulation and signalling pathways using gradient matching

A challenging problem in systems biology is parameter inference in mechanistic models of signalling pathways. In the present article, we investigate an approach based on gradient matching and nonparametric Bayesian modelling with Gaussian processes. We evaluate the method on two biological systems, related to the regulation of PIF4/5 in Arabidopsis thaliana, and the JAK/STAT signal transduction pathway

Robustness Analysis for Terminal Phases of Re-entry Flight

Advancements in the current practices used in robustness analysis for FCS design refinement by introducing a method that takes into account nonlinear effects of multiple uncertainties over the whole trajectory, to be used before robustness is finally assessed with MC analysis has been reported. Current practice in FCS robustness analysis for this kind of application mainly relies on the theory of linear time-invariant (LTI) systems. The method delivers feedback on the causes of requirement violation and adopts robustness criteria directly linked to the original mission or system requirements, such as those employed in MC analyses. The nonlinear robustness criterion proposed in the present work is based on the practical stability and/or finite time stability concepts. The practical stability property improves the accuracy in robustness evaluation with respect to frozen-time approaches, thus reducing the risk of discovering additional effects during robustness verification with Monte Carlo techniques

Precision Measurement of the Spin-Dependent Asymmetry in the Threshold Region of ^3He(e, e')

We present the first precision measurement of the spin-dependent asymmetry in the threshold region of ^3He(e,e′) at Q^2 values of 0.1 and 0.2(GeV/c)^2. The agreement between the data and nonrelativistic Faddeev calculations which include both final-state interactions and meson-exchange current effects is very good at Q^2 = 0.1(GeV/c)^2, while a small discrepancy at Q^2 = 0.2(GeV/c)^2 is observed

Tomato ionomic approach for food fortification and safety.

Food fortification is an issue of paramount of importance for people living both in developed and in developing countries. Among substances listed as "nutriceuticals", essential minerals have been recognised for their involvement in several healthy issues, involving all ages. In this frame, food plants are playing a pivotal role since their capability to compartmentalise ions and proteinmetal complexes in edible organs. Conversely, the accumulation of high metal levels in those organs may lead to safety problems. In the recent years, thanks to the availability of new and improved analytical apparatus in both ionic and genomic/transcrittomics areas, it is became feasible to couple data coming from plant physiology and genetics. Ionomics is the discipline that studies the cross-analysis of both data sets. Our group, in the frame of GenoPom project granted by MiUR, is interested to study the ionomics of tomatoes cultivars derived by breeding programmes in which wild relatives have been used to transfer several useful traits, such as resistance to biotic or abiotic stresses, fruit composition and textiture, etc. The introgression of the wild genome into the cultivated one produces new gene combinations. They might lead to the expression of some traits, such as increased or reduced adsorption of some metals and their exclusion or loading into edible organs, thus strongly involving the nutritional food value. Our final goal is to put together data coming from ions homeostasis and gene expression analyses, thus obtaining an ionomic tomato map related to ions absorption, translocation and accumulation in various plant organs, fruits included. To follow our hypothesis, we are studying the ionome of Solanum lycopersicum cv. M82 along with 76 Introgression Lines (ILs) produced by interspecific crosses between this cultivar and the wild species S. pennellii. These ILs are homozygous for small portions of the wild species genome introgressed into the domesticated M82 one. They are used as a useful tool for mapping QTL associated with many traits of interest. It is worthy to note that, until now, little information is available on QTL for ions accumulation in tomato. Moreover, as our knowledge, effects of new gene combinations in introgressed lines on ions uptake related to food safety have not been extensively studied. In this presentation we show results coming from the ionome analysis, carried out on S . lycopersicum M82 and several ILs. Plants were grown in pots in a greenhouse and watered with deionised water Thirty day-old plants were left to grow for 15 days in the presence of non-toxic concentration of Cd, Pb, As, Cr and Zn given combined. Leaves of all plants were then harvested and stored at -80°C for ionome and gene expression analyses. Preliminary results of ionome analysis of S. lycopersicum M82 and several ILs, carried out using an ICP-MS, showed that traits correlated to toxic metals and micronutrients accumulation in apical leaves were significantly modified in response to specific genetic backgrounds. Those results are perhaps due to the introgression of traits linked to uptake, translocation and accumulation of useful and/or toxic metal into plant apical leaves and to interactions of the wild type introgressed genomic regions with the cultivated genome. Also, data are shown on the identification and isolation of Solanum gene sequences related to ions uptake, translocation and accumulation, useful for further real-time gene expression evaluation in both cultivated and ILs during the treatments with the above-mentioned metals

Relative Comparison Kernel Learning with Auxiliary Kernels

In this work we consider the problem of learning a positive semidefinite kernel matrix from relative comparisons of the form: "object A is more similar to object B than it is to C", where comparisons are given by humans. Existing solutions to this problem assume many comparisons are provided to learn a high quality kernel. However, this can be considered unrealistic for many real-world tasks since relative assessments require human input, which is often costly or difficult to obtain. Because of this, only a limited number of these comparisons may be provided. In this work, we explore methods for aiding the process of learning a kernel with the help of auxiliary kernels built from more easily extractable information regarding the relationships among objects. We propose a new kernel learning approach in which the target kernel is defined as a conic combination of auxiliary kernels and a kernel whose elements are learned directly. We formulate a convex optimization to solve for this target kernel that adds only minor overhead to methods that use no auxiliary information. Empirical results show that in the presence of few training relative comparisons, our method can learn kernels that generalize to more out-of-sample comparisons than methods that do not utilize auxiliary information, as well as similar methods that learn metrics over objects

A hybrid approach to robustness analyses of flight control laws in re-entry applications

The present paper aims at improving the efficiency of the robustness analyses of flight control laws with respect to conventional techniques, especially when applied to vehicles following time-varying reference trajectories, such as in an atmospheric re-entry. A nonlinear robustness criterion is proposed, stemming from the practical stability framework, which allows dealing effectively with such cases. A novel approach is presented, which exploits the convexity of linear time varying systems, coupled to an approximate description of the original nonlinear system by a certain number of its time-varying linearizations. The suitability of the approximating systems is evaluated in a probabilistic fashion making use of the unscented transformation technique. The effectiveness and potentials of the method are ascertained by application to the robustness analysis of the longitudinal flight control laws of the Italian Aerospace Research Center (CIRA) experimental vehicle USV