arXiv subjects

Davide La Torre

Publications and source records attributed to Davide La Torre.

17 recordsLinked to original sources

arXiv2025

Identification of Cellular Automata on Spaces of Bernoulli Probability Measures

Classical Cellular Automata (CCAs) are a powerful computational framework for modeling global spatio-temporal dynamics with local interactions. While CCAs have been applied across numerous scientific fields, identifying the local rule that governs observed dynamics remains a challenging task. Moreover, the underlying assumption of deterministic cell states often limits the applicability of CCAs to systems characterized by inherent uncertainty. This study, therefore, focuses on the identification of Cellular Automata on spaces of probability measures (CAMs), where cell states are represented by probability distributions. This framework enables the modeling of systems with probabilistic uncertainty and spatially varying dynamics. Moreover, we formulate the local rule identification problem as a parameter estimation problem and propose a meta-heuristic search based on Self-adaptive Differential Evolution (SaDE) to estimate local rule parameters accurately from the observed data. The efficacy of the proposed approach is demonstrated through local rule identification in two-dimensional CAMs with varying neighborhood types and radii.

eess.SY↗

arXiv2025

Cellular Automata on Probability Measures

Classical Cellular Automata (CCAs) are a powerful computational framework widely used to model complex systems driven by local interactions. Their simplicity lies in the use of a finite set of states and a uniform local rule, yet this simplicity leads to rich and diverse dynamical behaviors. CCAs have found applications in numerous scientific fields, including quantum computing, biology, social sciences, and cryptography. However, traditional CCAs assume complete certainty in the state of all cells, which limits their ability to model systems with inherent uncertainty. This paper introduces a novel generalization of CCAs, termed Cellular Automata on Measures (CAMs), which extends the classical framework to incorporate probabilistic uncertainty. In this setting, the state of each cell is described by a probability measure, and the local rule operates on configurations of such measures. This generalization encompasses the traditional Bernoulli measure framework of CCAs and enables the study of more complex systems, including those with spatially varying probabilities. We provide a rigorous mathematical foundation for CAMs, demonstrate their applicability through concrete examples, and explore their potential to model the dynamics of random graphs. Additionally, we establish connections between CAMs and symbolic dynamics, presenting new avenues for research in random graph theory. This study lays the groundwork for future exploration of CAMs, offering a flexible and robust framework for modeling uncertainty in cellular automata and opening new directions for both theoretical analysis and practical applications.

nlin.CG↗

arXiv2024

Quantification of Non-stationary Power Quality Events: A New Index Based on $\ell_p$-norm of Energy

The present study proposes a new index to quantify the severity of non-stationary power quality (PQ) disturbance events. In particular, the severity of PQ events is estimated from their energy distribution in temporal-frequency space. The index essentially measures the $\ell_p$-norm between the energy distributions of an event and the nominal voltage signal. The efficacy of the new index is demonstrated considering a wide class of major non-stationary PQ events, including sag, swell, interruptions, oscillatory transients, and simultaneous events. The results of this investigation, with simulated, real and experimental data, convincingly demonstrate that the proposed index is generic, monotonic, easy to interpret, and can accurately quantify the severity of non-stationary events.

eess.SY↗

arXiv2024

Predicting Parkinson's disease evolution using deep learning

Parkinson's disease is a neurological condition that occurs in nearly 1% of the world's population. The disease is manifested by a drop in dopamine production, symptoms are cognitive and behavioural and include a wide range of personality changes, depressive disorders, memory problems, and emotional dysregulation, which can occur as the disease progresses. Early diagnosis and accurate staging of the disease are essential to apply the appropriate therapeutic approaches to slow cognitive and motor decline. Currently, there is not a single blood test or biomarker available to diagnose Parkinson's disease. Magnetic resonance imaging has been used for the past three decades to diagnose and distinguish between PD and other neurological conditions. However, in recent years new possibilities have arisen: several AI algorithms have been developed to increase the precision and accuracy of differential diagnosis of PD at an early stage. To our knowledge, no AI tools have been designed to identify the stage of progression. This paper aims to fill this gap. Using the "Parkinson's Progression Markers Initiative" dataset, which reports the patient's MRI and an indication of the disease stage, we developed a model to identify the level of progression. The images and the associated scores were used for training and assessing different deep-learning models. Our analysis distinguished four distinct disease progression levels based on a standard scale (Hoehn and Yah scale). The final architecture consists of the cascading of a 3DCNN network, adopted to reduce and extract the spatial characteristics of the RMI for efficient training of the successive LSTM layers, aiming at modelling the temporal dependencies among the data. Our results show that the proposed 3DCNN + LSTM model achieves state-of-the-art results by classifying the elements with 91.90\% as macro averaged OVR AUC on four classes

eess.IV↗

arXiv2023

Coevolution of Neural Architectures and Features for Stock Market Forecasting: A Multi-objective Decision Perspective

In a multi objective setting, a portfolio manager's highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of nondominated neural network models for further selection by the decision maker. A new coevolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the coevolution is posed as a multicriteria problem to evolve sparse and efficacious neural architectures. The well known dominance and decomposition based multiobjective evolutionary algorithms are augmented with a nongeometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the coevolution is augmented to accommodate the data based implications of distinct market behaviors prior to and during the ongoing COVID 19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized coevolution approach. The results on the NASDAQ index in pre and peri COVID time windows convincingly demonstrate that the proposed coevolution approach can evolve a set of nondominated neural forecasting models with better generalization capabilities.

cs.CE↗

arXiv2022

Competing control scenarios in probabilistic SIR epidemics on social-contact networks

A probabilistic approach to the epidemic evolution on realistic social-contact networks allows for characteristic differences among subjects, including the individual number and structure of social contacts, and the heterogeneity of the infection and recovery rates according to age or medical preconditions. Within our probabilistic Susceptible-Infectious-Removed (SIR) model on social-contact networks, we evaluate the `infection load' or `activation margin' of various control scenarios; by confinement, by vaccination, and by their combination. We compare the epidemic burden for subpopulations which apply competing or co-operative control strategies. The simulation experiments are conducted on randomised social-contact graphs that are designed to exhibit realistic person-person contact characteristics and which follow near `homogeneous' or `block-localised' subpopulation spreading. The scalarization method is used for the multi-objective optimization problem in which both the infection load is minimized and the extent to which each subpopulation's control strategy preference ranking is adhered to is maximized. We obtain the compounded payoff matrices for two subpopulations which impose contrasting control strategies, each according to their proper ranked control strategy preferences. The Nash equilibria, according to each subpopulation's compounded objective, and according to their proper ranking intensity, are discussed. Finally, the interaction effects of the control strategies are discussed and related to the type of spreading of the two subpopulations.

cs.SI↗

arXiv2022

Federated Deep Learning in Electricity Forecasting: An MCDM Approach

Large-scale data analysis is growing at an exponential rate as data proliferates in our societies. This abundance of data has the advantage of allowing the decision-maker to implement complex models in scenarios that were prohibitive before. At the same time, such an amount of data requires a distributed thinking approach. In fact, Deep Learning models require plenty of resources, and distributed training is needed. This paper presents a Multicriteria approach for distributed learning. Our approach uses the Weighted Goal Programming approach in its Chebyshev formulation to build an ensemble of decision rules that optimize aprioristically defined performance metrics. Such a formulation is beneficial because it is both model and metric agnostic and provides an interpretable output for the decision-maker. We test our approach by showing a practical application in electricity demand forecasting. Our results suggest that when we allow for dataset split overlapping, the performances of our methodology are consistently above the baseline model trained on the whole dataset.

math.OC↗

arXiv2021

Mixing Deep Learning and Multiple Criteria Optimization: An Application to Distributed Learning with Multiple Datasets

The training phase is the most important stage during the machine learning process. In the case of labeled data and supervised learning, machine training consists in minimizing the loss function subject to different constraints. In an abstract setting, it can be formulated as a multiple criteria optimization model in which each criterion measures the distance between the output associated with a specific input and its label. Therefore, the fitting term is a vector function and its minimization is intended in the Pareto sense. We provide stability results of the efficient solutions with respect to perturbations of input and output data. We then extend the same approach to the case of learning with multiple datasets. The multiple dataset environment is relevant when reducing the bias due to the choice of a specific training set. We propose a scalarization approach to implement this model and numerical experiments in digit classification using MNIST data.

cs.LG↗

arXiv2021

A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting

This study proposes a new framework to evolve efficacious yet parsimonious neural architectures for the movement prediction of stock market indices using technical indicators as inputs. In the light of a sparse signal-to-noise ratio under the Efficient Market hypothesis, developing machine learning methods to predict the movement of a financial market using technical indicators has shown to be a challenging problem. To this end, the neural architecture search is posed as a multi-criteria optimization problem to balance the efficacy with the complexity of architectures. In addition, the implications of different dominant trading tendencies which may be present in the pre-COVID and within-COVID time periods are investigated. An $ε-$ constraint framework is proposed as a remedy to extract any concordant information underlying the possibly conflicting pre-COVID data. Further, a new search paradigm, Two-Dimensional Swarms (2DS) is proposed for the multi-criteria neural architecture search, which explicitly integrates sparsity as an additional search dimension in particle swarms. A detailed comparative evaluation of the proposed approach is carried out by considering genetic algorithm and several combinations of empirical neural design rules with a filter-based feature selection method (mRMR) as baseline approaches. The results of this study convincingly demonstrate that the proposed approach can evolve parsimonious networks with better generalization capabilities.

cs.NE↗

arXiv2021

A vector logistic dynamical approach to epidemic evolution on interacting social-contact and production-capacity graphs

Population inhomogeneity, in the variation of the individual social contact networks and the individual infectious-recovery rates, renders the dynamics of infectious disease spreading uncertain. As a consequence the overlaying economical production network with its proper collaboration components is to extent impacted unpredictably. Our model proposes a `vector logistic' dynamical approach to SIS dynamics in a social contact network interacting with its economic capacity network. The probabilistic interpretation of the graph state in the vector logistic description provides a method to assess the effect of mean and variance of the infected on the production capacity and allows the strategic planning of social connectivity regulation. The impact of the epidemic mean effects and fluctuations on the production capacity is assessed according `cumulative',`majority' and `fragility' proxy measures.

physics.soc-ph↗

arXiv2019

A Multicriteria Macroeconomic Model with Intertemporal Equity and Spatial Spillovers

We analyze a macroeconomic model with intergenerational equity considerations and spatial spillovers, which gives rise to a multicriteria optimization problem. Intergenerational equity requires to add in the definition of social welfare a long run sustainability criterion to the traditional discounted utilitarian criterion. The spatial structure allows for the possibility of heterogeneiity and spatial diffusion implies that all locations within the spatial domain are interconnected via spatial spillovers. We rely on different techniques (scalarization, $ε$-constraint method and goal programming) to analyze such a spatial multicriteria problem, relying on numerical approaches to illustrate the nature of the trade-off between the discounted utilitarian and the sustainability criteria.

econ.TH↗

arXiv2019

Solving Inverse Problems for Steady-State Equations using A Multiple Criteria Model with Collage Distance, Entropy, and Sparsity

In this paper, we extend the previous method for solving inverse problems for steady-state equations using the Generalized Collage Theorem by searching for an approximation that not only minimizes the collage error but also maximizes the entropy and minimize the sparsity. In this extended formulation, the parameter estimation minimization problem can be understood as a multiple criteria problem, with three different and conflicting criteria: The generalized collage error, the entropy associated with the unknown parameters, and the sparsity of the set of unknown parameters. We implement a scalarization technique to reduce the multiple criteria program to a single criterion one, by combining all objective functions with different trade-off weights. Numerical examples confirm that the collage method produces good, but sub-optimal, results. A relatively low-weighted entropy term allows for better approximations while the sparsity term decreases the complexity of the solution in terms of the number of elements in the basis.

math.NA↗

arXiv2019

Transboundary Pollution Externalities: Think Globally, Act Locally?

We analyze the implications of transboundary pollution externalities on environmental policymaking in a spatial and finite time horizon setting. We focus on a simple regional optimal pollution control problem in order to compare the global and local solutions in which, respectively, the transboundary externality is and is not taken into account in the determination of the optimal policy by individual local policymakers. We show that the local solution is suboptimal and as such a global approach to environmental problems is effectively needed. Our conclusions hold true in different frameworks, including situations in which the spatial domain is either bounded or unbounded, and situations in which macroeconomic-environmental feedback effects are taken into account. We also show that if every local economy implements an environmental policy stringent enough, then the global average level of pollution will fall. If this is the case, over the long run the entire global economy will be able to achieve a completely pollution-free status.

econ.TH↗

arXiv2019

Optimal Control of Prevention and Treatment in a Basic Macroeconomic-Epidemiological Model

We analyze the optimal control of disease prevention and treatment in a basic SIS model. We develop a simple macroeconomic setup in which the social planner determines how to optimally intervene, through income taxation, in order to minimize the social cost, inclusive of infection and economic costs, of the spread of an epidemic disease. The disease lowers economic production and thus income by reducing the size of the labor force employed in productive activities, tightening thus the economy's overall resources constraint. We consider a framework in which the planner uses the collected tax revenue to intervene in either prevention (aimed at reducing the rate of infection) or treatment (aimed at increasing the speed of recovery). Both optimal prevention and treatment policies allow the economy to achieve a disease-free equilibrium in the long run but their associated costs are substantially different along the transitional dynamic path. By quantifying the social costs associated with prevention and treatment we determine which policy is most cost-effective under different circumstances, showing that prevention (treatment) is desirable whenever the infectivity rate is low (high).

econ.TH↗

arXiv2012

A Goal Programming Model with Satisfaction Function for Risk Management and Optimal Portfolio Diversification

We extend the classical risk minimization model with scalar risk measures to the general case of set-valued risk measures. The problem we obtain is a set-valued optimization model and we propose a goal programming-based approach with satisfaction function to obtain a solution which represents the best compromise between goals and the achievement levels. Numerical examples are provided to illustrate how the method works in practical situations.

q-fin.RM↗

arXiv2003

Nonparametric estimation of distribution and density functions in presence of missing data: an IFS approach

In this paper we consider a class of nonparametric estimators of a distribution function F, with compact support, based on the theory of IFSs. The estimator of F is tought as the fixed point of a contractive operator T defined in terms of a vector of parameters p and a family of affine maps W which can be both depend of the sample (X_1, X_2, ...., X_n). Given W, the problem consists in finding a vector p such that the fixed point of T is ``sufficiently near'' to F. It turns out that this is a quadratic constrained optimization problem that we propose to solve by penalization techniques. If F has a density f, we can also provide an estimator of f based on Fourier techniques. IFS estimators for F are asymptotically equivalent to the empirical distribution function (e.d.f.) estimator. We will study relative efficiency of the IFS estimators with respect to the e.d.f. for small samples via Monte Carlo approach. For well behaved distribution functions F and for a particular family of so-called wavelet maps the IFS estimators can be dramatically better than the e.d.f. (or the kernel estimator for density estimation) in presence of missing data, i.e. when it is only possibile to observe data on subsets of the whole support of F. This research has also produced a free package for the R statistical environment which is ready to be used in applications.

math.ST↗

arXiv2001

Approximating distribution functions by iterated function systems

In this paper an iterated function system on the space of distribution functions is built. The inverse problem is introduced and studied by convex optimization problems. Some applications of this method to approximation of distribution functions and to estimation theory are given.

math.ST↗