Leopoldo Milano

A novel neural network-based survival analysis model

A feedforward neural network architecture aimed at survival probability estimation is presented which generalizes the standard, usually linear, models described in literature. The network builds an approximation to the survival probability of a system at a given time, conditional on the system features. The resulting model is described in a hierarchical Bayesian framework. Experiments with synthetic and real world data compare the performance of this model with the commonly used standard ones.

Neural networks in astronomy

In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).

Towards measuring variations of Casimir energy by a superconducting cavity.

We consider a Casimir cavity, one plate of which is a thin superconducting film. We show that when the cavity is cooled below the critical temperature for the onset of superconductivity, the sharp variation (in the far infrared) of the reflection coefficient of the film engenders a variation in the value of the Casimir energy. Even though the relative variation in the Casimir energy is very small, its magnitude can be comparable to the condensation energy of the superconducting film, and this gives rise to a number of testable effects, including a significant increase in the value of the critical magnetic field, required to destroy the superconductivity of the film. The theoretical ground is therefore prepared for the first experiment ever aimed at measuring variations of the Casimir energy itself.

Genetic approach helps to speed classical Price algorithm for global optimization

In this paper is presented an hybrid algorithm for finding the absolute extreme point of a multimodal scalar function of many variables. The algorithm is suitable when the objective function is expensive to compute, the computation can be affected by noise and/or partial derivatives cannot be calculated. The method used is a genetic modification of a previous algorithm based on the Price’s method. All information about behavior of objective function collected on previous iterates are used to chose new evaluation points. The genetic part of the algorithm is very effective to escape from local attractors of the algorithm and assures convergence in probability to the global optimum. The proposed algorithm has been tested on a large set of multimodal test problems outperforming both the modified Price’s algorithm and classical genetic approach.

Real-time digital control of optical interferometers by the mechanical-modulation technique

We discuss the application of digital systems to the automatic control of dual-wave optical interferometers. We show that, if the mechanical-modulation technique is used for error-signal extraction, digital techniques can be used both for error-signal extraction and for control-signal generation. Therefore, apart from two front/end amplifiers that are necessary to match the dynamics of the detectors and actuators to the dynamics of the analog-to-digital converters and digital-to-analog converters, no other analog devices are required. In particular, the mechanical-modulation technique requires the synchronous demodulation of the photodiode output signal. Hence we need to implement a digital lock-in amplifier whose algorithm is described here. Finally, we describe one of the possible applications of this digital control procedure, such as the control of a classic Mach-Zehnder interferometer in air.

The semi-detached system RT Persei and its light curve

The observedV light curve of the eclipsing binary RT Persei has been analysed by four different methods of solution to get the geometrical and photometric elements of the system. The results show a good agreement within about 6%.The system shows a variable light curve and a tentative hypothesis is made to explain this behaviour by considering a dynamical instability that may be the cause of circulating matter through the system and of the variability of the period.

Hybrid neural networks for frequency estimation of unevenly sampled data

We present a hybrid system composed of a neural network based estimator system and genetic algorithms. It uses an unsupervised Hebbian nonlinear neural algorithm to extract the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. We generalize this method to avoid an interpolation preprocessing step and to improve the performance by using a new stop criterion to avoid over fitting. Furthermore, genetic algorithms are used to optimize the neural net weight initialization.

Digitally controlled interferometer prototype for gravitational wave detection

In this article, we describe the architecture of the 3 m suspended Michelson interferometer prototype for gravitational wave detection which is operational in Napoli. The characteristic which makes this interferometer different from the existing ones is the digital implementation of the control system, the monitoring system, the data acquisition system, and the archiving system. This architecture makes this interferometer a good test bench for the study, the development, and the test of general techniques for the automatic control of interferometers for gravitational wave detection. In particular, it is now being used for the development and the test of some subsystems of the very long baseline interferometric VIRGO antenna for gravitational wave detection. [The Virgo Project, Final Design of the Italian–French large base interferometric antenna Virgo for gravitational wave detection of which the authors are proponents and in whose construction the Authors are collaborating (INFN, Italy, and CNRS, France,...

Neural Networks for Spectral Analysis of Unevenly Sampled Data

In this paper we present a neural network based estimator system which performs well the frequency extraction from unevenly sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to extract the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies.

High accuracy digital temperature control for a laser diode

A digital servo‐loop was implemented to control the temperature of a laser diode. By using a conditionally stable loop we obtained a temperature stability of about ±20 μK over periods of hours.