Minimal resources identifiability and estimation of quantum channels

We characterize and discuss the identifiability condition for quantum process tomography, as well as the minimal experimental resources that ensure a unique solution in the estimation of quantum channels, with both direct and convex optimization methods. A convenient parametrization of the constrained set is used to develop a globally converging Newton-type algorithm that ensures a physically admissible solution to the problem. Numerical simulation is provided to support the results and indicate that the minimal experimental setting is sufficient to guarantee good estimates.     

Collective behavior of networks with linear (VLSI) integrate-and-fire neurons

We analyze in detail the statistical properties of the spike emission process of a canonical integrate-and-fire neuron, with a linear integrator and a lower bound for the depolarization, as often used in VLSI implementations (Mead, 1989). The spike statistics of such neurons appear to be qualitatively similar to conventional (exponential) integrate-and-fire neurons, which exhibit a wide variety of characteristics observed in cortical recordings. We also show that, contrary to current opinion, the dynamics of a network composed of such neurons has two stable fixed points, even in the purely excitatory network, corresponding to two different states of reverberating activity. The analytical results are compared with numerical simulations and are found to be in good agreement.     

Quantitative Assessment of a Peer-to-peer Cooperative Infrastructure Using Stochastic Well-Formed Nets

Traditional support tools for software engineers, normally based on a client-server architecture, are unsuitable to deal with the new issues emerging from the current (and future) cooperative work scenarios (where connectivity is intrinsically transient, the number of interacting partners dynamically changes, etc.). This paper presents a quantitative assessment of a fully decentralized, peer-to-peer, cooperative infrastructure. Stochastic Well-Formed Nets (SWNs) modelling the new peer-to-peer architecture, and a traditional (client-server) one, are developed and analysed: we used SWNs for their ability to directly exploit the symmetries intrinsically present in the modelled systems, thus greatly reducing the complexity of the analysis. The main goal is to compare the impact of the two alternative protocols on the collaborative work. Together with the performance figures of interest, methodological issues concerning the choice of the most appropriate model abstraction level, the adoption of a compositional modelling approach, and the management of the model complexity are also discussed.     

Shape optimization for drag reduction in linked bodies using evolution strategies

We present results from the shape optimization of linked bodies for drag reduction in simulations of incompressible flow at moderate Reynolds numbers. The optimization relies on the covariance matrix adaptation evolution strategy (CMA-ES) and the flow simulations use vortex methods with the Brinkman penalization to enforce boundary conditions in complex bodies. We exploit the inherent parallelism of CMA-ES, by implementing a multi-host framework which allows for the distribution of the expensive cost function evaluations across parallel architectures, without being limited to one computing facility. This study repeats in silico for the first time Ingo Rechenberg’s pioneering wind tunnel experiments for drag reduction that led to the inception of evolution strategies. The simulations confirm that the results of these experimental studies indicate a flat plate is not the optimal solution for drag reduction in linked bodies. We present the vorticity field of the flow and identify the governing mechanisms for this drag reduction by the slightly corrugated linked plate configuration.     

A variational integrators approach to second order modeling and identification of linear mechanical systems

The theory of variational integration provides a systematic procedure to discretize the equations of motion of a mechanical system, preserving key properties of the continuous time flow. The discrete-time model obtained by variational integration theory inherits structural conditions which in general are not guaranteed under general discretization procedures. We discuss a simple class of variational integrators for linear second order mechanical systems and propose a constrained identification technique which employs simple linear transformation formulas to recover the continuous time parameters of the system from the discrete-time identified model. We test this approach on a simulated eight degrees of freedom system and show that the new procedure leads to an accurate identification of the continuous-time parameters of second-order mechanical systems starting from discrete measured data.     

Design and control of an IPMC wormlike robot.

This paper presents an innovative wormlike robot controlled by cellular neural networks (CNNs) and made of an ionic polymer-metal composite (IPMC) self-actuated skeleton. The IPMC actuators, from which it is made of, are new materials that behave similarly to biological muscles. The idea that inspired the work is the possibility of using IPMCs to design autonomous moving structures. CNNs have already demonstrated their powerfulness as new structures for bio-inspired locomotion generation and control. The control scheme for the proposed IPMC moving structure is based on CNNs. The wormlike robot is totally made of IPMCs, and each actuator has to carry its own weight. All the actuators are connected together without using any other additional part, thereby constituting the robot structure itself. Worm locomotion is performed by bending the actuators sequentially from "tail" to "head," imitating the traveling wave observed in real-world undulatory locomotion. The activation signals are generated by a CNN. In the authors' opinion, the proposed strategy represents a promising solution in the field of autonomous and light structures that are capable of reconfiguring and moving in line with spatial-temporal dynamics generated by CNNs.     

Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches

Genetic programming researchers have shown a growing interest in the study of gene regulatory networks in the last few years. Our team has also contributed to the field, by defining two systems for the automatic reverse engineering of gene regulatory networks called GRNGen and GeNet. In this paper, we revise this work by describing in detail the two approaches and empirically comparing them. The results we report, and in particular the fact that GeNet can be used on large networks while GRNGen cannot, encourage us to pursue the study of GeNet in the future. We conclude the paper by discussing the main research directions that we are planning to investigate to improve GeNet.     

Comparative evaluation of the olive oil given by a new processing system

To evaluate the level of quality of the olive oil given by a new processing system we compared it with the percolation oil, which is known to be of high quality. The new product was characterized by the following: (i) higher contents of tocopherols, tyrosol- and hydroxytyrosol-aglycones, volatile aromatic substances, sterols, triterpene alcohols and lipochromes, and higher values of 1,2-diglycerides/1,3-diglycerides and campesterol/stigmasterol ratios, colour indices and turbidity; (ii) lower contents of waxes and triterpene dialcohols, and lower values for spectrophotometric indices, carbonyl index, Wolff's ratio (K₂₃₂/K₂₇₀), acidity, and peroxide number; (iii) similar contents of phenols, o- diphenols, free tyrosol, free hydroxytyrosol, triglycerides, stigmastadienes, and aliphatic alcohols, similar sensory score, and similar value of resistance to autoxidation, alcohol index, overall quality indices, and fatty acid composition.