Pairwise Registration by Local Orientation Cues

Inspired by recent work on robust and fast computation of 3D Local Reference Frames (LRFs), we propose a novel pipeline for coarse registration of 3D point clouds. Key to the method are: (i) the observation that any two corresponding points endowed with an LRF provide a hypothesis on the rigid motion between two views, (ii) the intuition that feature points can be matched based solely on cues directly derived from the computation of the LRF, (iii) a feature detection approach relying on a saliency criterion which captures the ability to establish an LRF repeatably. Unlike related work in literature, we also propose a comprehensive experimental evaluation based on diverse kinds of data (such as those acquired by laser scanners, Kinect and stereo cameras) as well as on quantitative comparison with respect to other methods. We also address the issue of setting the many parameters that characterize coarse registration pipelines fairly and realistically. The experimental evaluation vouches that our method can handle effectively data acquired by different sensors and is remarkably fast.     

Feedforward networks in financial predictions: the future that modifies the present

The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the 'self-momentum', 'Freud' and 'Jung' rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated 'learning to learn' characteristics are now under study and test.     

Development and applications of surface-activated chemical ionization

This review regards the recently developed ionization source named surface-activated chemical ionization (SACI) that employs an interaction with a surface placed at low voltage for the activation of the ionization of sample molecules to increase the sensitivity in the analysis of various compounds of biological and clinical interest. These results are due to the strong chemical noise decrease and the increase of ionization efficiency. This ionization source has been employed for the analysis of various compounds of different molecular mass and polarity (addicted and pharmaceutical drugs, amino acids, steroids, peptides, and proteins). The SACI development theoretical mechanism, benefits, disadvantages, applications, and future developments are reported and discussed.     

Estimation of predictive accuracy in survival analysis using R and S-PLUS

When the purpose of a survival regression model is to predict future outcomes, the predictive accuracy of the model needs to be evaluated before practical application. Various measures of predictive accuracy have been proposed for survival data, none of which has been adopted as a standard, and their inclusion in statistical software is disregarded. We developed the surev library for R and S-PLUS, which includes functions for evaluating the predictive accuracy measures proposed by Schemper and Henderson. The library evaluates the predictive accuracy of parametric regression models and of Cox models. The predictive accuracy of the Cox model can be obtained also when time-dependent covariates are included because of non-proportional hazards or when using Bayesian model averaging. The use of the library is illustrated with examples based on a real data set.     

Virtual Organizations in Arigatoni

Arigatoni is a lightweight overlay network that deploys the Global Computing Paradigm over the Internet. Communication for over the behavioral units of the overlay is performed by a simple resource discovery protocol (RDP). Basic Global Computers Units (GC) can communicate by first registering to a brokering service and then by mutually asking and offering services.Colonies and communities are the main entities in the model. A colony is a simple virtual organization composed by exactly one leader and some set (possibly empty) of individuals. A community is a raw set of colonies and global computers (think it as a soup of colonies and global computer without a leader).We present an operational semantics via a labeled transition system, that describes the main operations necessary in the Arigatoni model to perform leader negotiation, joining/leaving a colony, linking two colonies and moving one GC from one colony to another. Our formalization results to be adequate w.r.t. the algorithm performing peer logging/delogging and colony aggregation.     

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.     

An Integrated Assessment of the Impacts of Changing Climate Variability on Agricultural Productivity and Profitability in an Irrigated Mediterranean Catchment

Climate change is likely to have a profound effect on many agricultural variables, although the extent of its influence will vary over the course of the annual farm management cycle. Consequently, the effect of different and interconnected physical, technical and economic factors must be modeled in order to estimate the effects of climate change on agricultural productivity. Such modeling commonly makes use of indicators that summarize the among environmental factors that are considered when farmers plan their activities. This study uses net evapotranspiration (ET_N), estimated using EPIC, as a proxy index for the physical factors considered by farmers when managing irrigation. Recent trends suggest that the probability distribution function of ET_N may continue to change in the near future due to changes in the irrigation needs of crops. Also, water availability may continue to vary due to changes in the rainfall regime. The impacts of the uncertainties related to these changes on costs are evaluated using a Discrete Stochastic Programming model representing an irrigable Mediterranean area where limited water is supplied from a reservoir. In this context, adaptation to climate change can be best supported by improvements to the collective irrigation systems, rather than by measures aimed at individual farms such as those contained within the rural development policy.     

Geometry and Stacking Sequence Effect on Composite Spinnaker Pole's Stiffness: Experimental and Numerical Analysis

Composite materials are widely employed in sailing sports, a possible application is for the mast pole or other sail poles. In the paper the attention is focused on the spinnaker poles mechanical performances; in particular the focus is on axial and ring compressive properties of three different carbon fibre/epoxy resin spinnaker poles, to investigate both the diameter and stacking sequence effect on the mechanical performance of the structure. Starting from the stacking sequence used in the production of a particular spinnaker pole, the effect of a lamina at 0° in the middle of wall thickness is investigated with the purpose to obtain a more stiff structure. Moreover to test the proposed stacking sequence on different size products, a prototype with lower diameter is realized. To properly evaluate axial and ring stiffness, axial compression test and ring stiffness one are performed. Then a numerical model is developed to support the design of the finished product: A simple and versatile numerical analysis (FEA with software ANSYS), by simulating ring stiffness and pull-direction compression tests, is carried out in elastic regime. Such model should be suitable for designing and/or verifying the mechanical performance of pole structures, even though differing from those above described, for materials, geometry and stacking sequence.     

An experimental evaluation of cross-layer routing in a wireless mesh backbone

Over the latest few years, cross-layer design in wireless networks has drawn great attention from the research community. One of the main arguments in favor of such techniques is that the hop-count metric alone is not enough to capture the specificities of wireless links (e.g., interferences, collisions, fading). In this paper, we address a simple yet fundamental question: What are the real improvements that cross-layering can bring to routing performance when compared to the simple hop-count metric? In our experiments, we consider the backbone of a real wireless mesh network composed of 12 routers deployed in an office building. We focus on the stability of routes and their persistence. In spite of the nature of cross-layer metrics that take into account information from different layers, lets them be very reactive to changes, we observe that using these metrics, pairs of nodes tend to mainly use the same set of two or three routes between them.