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

Quantum phase transitions in fully connected spin models: an entanglement perspective

We consider a set of fully connected spins models that display first- or second-order transitions and for which we compute the ground-state entanglement in the thermodynamical limit. We analyze several entanglement measures (concurrence, R\'enyi entropy, and negativity), and show that, in general, discontinuous transitions lead to a jump of these quantities at the transition point. Interestingly, we also find examples where this is not the case.Comment: 9 pages, 7 figures, published versio

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

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

Cavallo's Multiplier for in situ Generation of High Voltage

A classic electrostatic induction machine, Cavallo's multiplier, is suggested for in situ production of very high voltage in cryogenic environments. The device is suitable for generating a large electrostatic field under conditions of very small load current. Operation of the Cavallo multiplier is analyzed, with quantitative description in terms of mutual capacitances between electrodes in the system. A demonstration apparatus was constructed, and measured voltages are compared to predictions based on measured capacitances in the system. The simplicity of the Cavallo multiplier makes it amenable to electrostatic analysis using finite element software, and electrode shapes can be optimized to take advantage of a high dielectric strength medium such as liquid helium. A design study is presented for a Cavallo multiplier in a large-scale, cryogenic experiment to measure the neutron electric dipole moment.Comment: 9 pages, 10 figure

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

An introduction to the Generalized Parton Distributions

The concepts of Generalized Parton Distributions (GPD) are reviewed in an introductory and phenomenological fashion. These distributions provide a rich and unifying picture of the nucleon structure. Their physical meaning is discussed. The GPD are in principle measurable through exclusive deeply virtual production of photons (DVCS) or of mesons (DVMP). Experiments are starting to test the validity of these concepts. First results are discussed and new experimental projects presented, with an emphasis on this program at Jefferson Lab.Comment: 5 pages, 3 figures Proc. Int. Conf. on Quark Nuclear Physics (QNP2002), to be published in Eur. Phys. Jour.

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

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