Ricerc@Sapienza

This paper examines the compliance behaviour of a taxpayer endowed with a stochastic income, taking into account dynamical factors as public and private investments, within a stochastic control framework. Assuming logarithmic utilities and thanks to a suitable rewrite of the problem, we provide an existence and uniqueness result for the solution of the Hamilton–Jacobi–Bellman equation associated to the control problem, and we rely on a symbolic and numerical algorithm to study its solution.

Urban planning has long been introduced into the territorial classification elements as belonging to integrated waste cycle management. Within such framework, types of urban hygiene are defined and described. In particular, the General Regulatory Plan of the City of Rome has established that areas and facilities for separate collection of waste belong to the secondary urbanization works to be identified by executive planning, as well as for temporary collection, compacting and conveying inert and bulky waste.

In this paper, the problem of the online modeling of nonlinear speech signals is addressed. In particular, the goal of this work is to provide a nonlinear model yielding the best tradeoff between performance results and required computational resources. Functional link adaptive filters were proved to be an effective model for this problem, providing the best performance when trigonometric expansion is used as a nonlinear transformation.

Neural networks require a careful design in order to perform properly on a given task. In particular, selecting a good activation function (possibly in a data-dependent fashion) is a crucial step, which remains an open problem in the research community. Despite a large amount of investigations, most current implementations simply select one fixed function from a small set of candidates, which is not adapted during training, and is shared among all neurons throughout the different layers. However, neither two of these assumptions can be supposed optimal in practice.

The degree of diffusion of hypercomplex algebras in adaptive and non-adaptive filtering research topics is growing faster and faster. The debate today concerns the usefulness and the benefits of representing multidimensional systems by means of these complicated mathematical structures and the criterions of choice between one algebra or another. This paper proposes a simple comparison between two isodimensional algebras (quaternions and tessarines) and shows by simulations how different choices may determine the system performance.

In this paper, we propose a deep recurrent neural network (DRNN), based on the Long Short-Term Memory (LSTM) unit, for the separation of drum and bass sources from a monaural audio track. In particular, a single DRNN with a total of six hidden layers (three feedforward and three recurrent) is used for each original source to be separated. In this work, we limit our attention to the case of only two, challenging sources: drum and bass. Some experimental results show the effectiveness of the proposed approach with respect to another state-of-the-art method.

In this paper, several classification methods are applied for modeling financial time series with the aim to predict the trend of successive prices. By using a suitable embedding technique, a pattern of past prices is assigned a class if the variation of the next price is over, under or stable with respect to a given threshold. Furthermore, a sensitivity analysis is performed in order to verify if the value of such a threshold influences the prediction accuracy.

With reference to the public-private partnership procedures for the enhancement of the public property assets, in this paper an innovative methodology for assessing the financial conveniences of the parties involved (private investor and Public Administration) is proposed.