A Round Robin exercise was implemented by ESA to compare different classification methods in detecting clouds from images taken by the PROBA-V sensor. A high-quality dataset of 1350 reflectances and Clear/Cloudy corresponding labels had been prepared by ESA in the framework of the exercise. Motivated by both the experience acquired by one of the authors in this exercise and the availability of such a reliable annotated dataset, we present a full assessment of the methodology proposed therein. Our objective is also to investigate specific issues related to cloud detection when remotely sensed images comprise only a few spectral bands in the visible and near-infrared. For this purpose, we consider a bunch of well-known classification methods. First, we demonstrate the feasibility of using a training dataset semi-automatically obtained from other accurate algorithms. In addition, we investigate the effect of ancillary information, e.g., surface type or climate, on accuracy. Then we compare the different classification methods using the same training dataset under different configurations. We also perform a consensus analysis aimed at estimating the degree of mutual agreement among classification methods in detecting Clear or Cloudy sky conditions.

A limitation of current modeling studies in waterborne diseases (one of the leading causesof death worldwide) is that the intrinsic dynamics of the pathogens is poorly addressed, leadingto incomplete, and often, inadequate understanding of the pathogen evolution and its impact ondisease transmission and spread. To overcome these limitations, in this paper, we consider an ODEsmodel with bacterial growth inducing Allee effect. We adopt an adequate functional response tosignificantly express the shape of indirect transmission. The existence and stability of biologicallymeaningful equilibria is investigated through a detailed discussion of both backward and Hopfbifurcations. The sensitivity analysis of the basic reproduction number is performed. Numericalsimulations confirming the obtained results in two different scenarios are shown.

We show that the First-Passage-Time probability distribution of a Lévy time-changed Brownian motion with drift is solution of a time fractional advection-diffusion equation subject to initial and boundary conditions; the Caputo fractional derivative with respect to time is considered. We propose a high order compact implicit discretization scheme for solving this fractional PDE problem and we show that it preserves the structural properties (non-negativity, boundedness, time monotonicity) of the theoretical solution, having to be a probability distribution. Numerical experiments confirming such findings are reported. Simulations of the sample paths of the considered process are also performed and used to both provide suitable boundary conditions and to validate the numerical results.

We focus on interacting neurons organized in a block-layered network devoted to the information processing from the sensory system to the brain. Specifically, we consider the firing activity of olfactory sensory neurons, periglomerular, granule and mitral cells in the context of the neuronal activity of the olfactory bulb. We propose and investigate a stochastic model of a layered and modular network to describe the dynamic behavior of each prototypical neuron, taking into account both its role (excitatory/inhibitory) and its location within the network. We adopt specific Gauss-Markov processes suitable to provide reliable estimates of the firing activity of the different neurons, given their linkages. Furthermore, we study the impact of selective excitation/inhibition on the information transmission by means of simulations and numerical estimates obtained through a Volterra integral approach.

Measures and methods used in financial sector to quantify risk, have been recently applied to cyber world. The aim is to help organizations to improve risk management strategies and to wisely plan investments in cyber security. On the other hand, they are useful instruments for insurance companies in pricing cyber insurance contracts and setting the minimum capital requirements defined by the regulators. In this paper we propose an estimation of Value at Risk (VaR), referred to as Cyber Value at Risk in cyber security domain, and Tail Value at risk (TVaR). The data breach information we use is obtained from the 'Chronology of data breaches' compiled by the Privacy Rights Clearinghouse.

In the last decades companies worldwide are facing a new kind of risk, namely cyber risk, that has emerged as one of the top challenges in risk management. Insurance was only recently applied to cyber world and it is increasingly becoming part of the risk management process, posing many challenges to actuaries. One of the main issues is the lack of data, in particular nancial ones. The aim of the paper is to point out the peculiarities of cyber insurance contracts with respect to the classical non life insurance ones both from the insurer and the insured's perspective. Therefore, the main actuarial principles that are fundamental to any valu- ation in cyber context are discussed. An illustrative example is proposed where the Chronology of Data Breaches provided by the Privacy Rights Clearing House is deeply analyzed. The most suitable distributions to represent the frequency and the severity of the reported cyber incidents are examined and the value at risk measure is estimated. Then, two ex- emplifying cases oer the assessment of both the premium required by the insurer and the indierence premium that the insured is willing to pay. Even though this research is still preliminary and shows some limits highlighted by the authors, it could offer useful information to better un- derstand this peculiar kind of insurance policies.

Cloud detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking can be translated directly into significant uncertainty in the retrieved downstream geophysical products. The problem is particularly challenging when only of a limited number of spectral bands is available, and thermal infrared bands are lacking. This is the case of Proba-V instrument, for which the European Space Agency (ESA) carried out a dedicated Round Robin exercise, aimed at intercomparing several cloud detection algorithms to better understand their advantages and drawbacks for various clouds and surface conditions, and to learn lessons on cloud detection in the VNIR and SWIR domain for land and coastal water remote sensing. The present contribution is aimed at a thorough quality assessment of the results of the cloud detection approach we proposed, based on Cumulative Discriminant Analysis. Such a statistical method relies on the empirical cumulative distribution function of the measured reflectance in clear and cloudy conditions to produce a decision rule. It can be adapted to the user's requirements in terms of preferred levels for both type I and type II errors. In order to obtain a fully automatic procedure, we choose as a training dataset a subset of the full Proba-V scenes for which a cloud mask is estimated by a consolidated algorithm (silver standard), that is from either SEVIRI, MODIS or both sensors. Within this training set, different subsets have been setup according to the different types of surface underlying scenes (water, vegetation, bare land, urban, and snow/ice). We present the analysis of the cloud classification errors for a range of such test scenes to yield important inferences on the efficiency and accuracy of the proposed methodology when applied to different types of surfaces.

As discussed in the recent literature, several innovative car insurance concepts are proposed in order to gain advantages both for insurance companies and for drivers. In this context, the "pay-how-you-drive" paradigm is emerging, but it is not thoroughly discussed and much less implemented. In this paper, we propose an approach in order to identify the driver behavior exploring the usage of unsupervised machine learning techniques. A real-world case study is performed to evaluate the effectiveness of the proposed solution. Furthermore, we discuss how the proposed model can be adopted as risk indicator for car insurance companies.

A specific kind of insurance that is emerging within the domain of cyber-systems is that of cyber-insurance. Cyber-insurance is the transfer of financial risk associated with network and computer incidents to a third party. Insurance companies are increasingly offering such policies, in particular in the USA, but also in Europe. The emerging trends in cyber insurance raise a number of unique challenges and force actuaries to reconsider how to think about underwriting, pricing and aggregation risk. Aim of this contribution is to offer a review of the recent literature on cyber risk management in the actuarial field. Moreover, basing on the most significant results in IT domain, we outline possible synergies between the two lines of research.

The recognition of spatial patterns within agricultural fields, presenting similar yield potential areas, stable through time, is very important for optimizing agricultural practices. This study proposes the evaluation of different clustering methodologies applied to multispectral satellite time series for retrieving temporally stable (constant) patterns in agricultural fields, related to within-field yield spatial distribution. The ability of different clustering procedures for the recognition and mapping of constant patterns in fields of cereal crops was assessed. Crop vigor patterns, considered to be related to soils characteristics, and possibly indicative of yield potential, were derived by applying the different clustering algorithms to time series of Landsat images acquired on 94 agricultural fields near Rome (Italy). Two different approaches were applied and validated using Landsat 7 and 8 archived imagery. The first approach automatically extracts and calculates for each field of interest (FOI) the Normalized Difference Vegetation Index (NDVI), then exploits the standard K-means clustering algorithm to derive constant patterns at the field level. The second approach applies novel clustering procedures directly to spectral reflectance time series, in particular: (1) standard K-means; (2) functional K-means; (3) multivariate functional principal components clustering analysis; (4) hierarchical clustering. The different approaches were validated through cluster accuracy estimates on a reference set of FOIs for which yield maps were available for some years. Results show that multivariate functional principal components clustering, with an a priori determination of the optimal number of classes for each FOI, provides a better accuracy than those of standard clustering algorithms. The proposed novel functional clustering methodologies are effective and efficient for constant pattern retrieval and can be used for a sustainable management of agricultural fields, depending on farming systems and environmental conditions in different regions.

Stochastic processes are the formal representation of real systems whose evolution in time or space can be assumed as random. This contribution summarizes the necessary mathematical background material on the topic, including terminology and notation. It also illustrates the Markov property (or "lack of memory") of stochastic processes and shows examples of the main Markov processes, that are particularly relevant in applications. Indications are also given on the techniques for the numerical investigation of such processes. Extensive references for both advanced theory and biological/biochemical applications are provided.

An intraguild predator-prey model with a carrying capacity proportional to the biotic resource, is generalized by introducing a Holling type II functional response. The longtime behaviour of solutions is analyzed and, in particular, absorbing sets in the phase space are determined. The existence of biologically meaningful equilibria (boundary and internal equilibria) has been investigated. Linear and nonlinear stability conditions for biologically meaningful equilibria are performed. Finally, numerical simulations on different regimes of coexistence and extinction of the involved populations have been shown.

Mortality models play a basic role in the evaluation of longevity risk by demographers and actuaries. Their performance strongly depends on the different patterns shown by mortality data in different countries. A comprehensive quantitative comparison of the most used methods for forecasting mortality is presented, aimed at evaluating both the goodness of fit and the forecasting performance of these mortality models on Italian demographic data. First, the classical Lee-Carter model is compared to some generalizations that change the order of Singular Value Decomposition approximation and include cohort effects. Then one-way and two-way functional data approaches are considered. Such an analysis extends the current literature on Italian mortality data, on both the number of considered models and their rigorous assessment. Results indicate that generally functional models outperform the classical ones; unfortunately, even if the cohort effect is quite substantial, a suitable procedure for its robust and efficient evaluation is yet to be proposed. To this end, a viable correction for cohort effects is suggested and its performance tested on some of the presented models.

A Leaky Integrate-and-Fire (LIF) model with stochastic current-based linkages is considered to describe the firing activity of neurons interacting in a (2. ×. 2)-size feed-forward network. In the subthreshold regime and under the assumption that no more than one spike is exchanged between coupled neurons, the stochastic evolution of the neuronal membrane voltage is subject to random jumps due to interactions in the network. Linked Gauss-Diffusion processes are proposed to describe this dynamics and to provide estimates of the firing probability density of each neuron. To this end, an iterated integral equation-based approach is applied to evaluate numerically the first passage time density of such processes through the firing threshold. Asymptotic approximations of the firing densities of surrounding neurons are used to obtain closed-form expressions for the mean of the involved processes and to simplify the numerical procedure. An extension of the model to an (N ×. N)-size network is also given. Histograms of firing times obtained by simulations of the LIF dynamics and numerical firings estimates are compared.

This study shows the land cover mapping accuracy retrievable by the TASI-600 thermal airborne multispectral sensor and describes some of the classification results tested on the thermal preprocessed data for a rural area. In the paper is provided an overview of the principal TASI-600 characteristics, i.e. 32 spectral bands in the 8.0-11.5 ?m spectral range, and land cover classification performances. A full assessment of the TASI-600 spectral bands has been also obtained by ranking them in order to understanding their role in land cover classification. Results accuracies have been validated using available ground truth. The study highlights that the new generation of multi/hyperspectral thermal sensors opens up interesting opportunities for accurate land cover classification.

A model is proposed to describe the spike-frequency adaptation observed in many neuronal systems. We assume that adaptation is mainly due to a calcium-activated potassium current, and we consider two coupled stochastic differential equations for which an analytical approach combined with simulation techniques and numerical methods allow to obtain both qualitative and quantitative results about asymptotic mean firing rate, mean calcium concentration and the firing probability density. A related algorithm, based on the Hazard Rate Method, is also devised and described.

Experiments of cell migration and chemotaxis assays have been classically performed in the so-called Boyden Chambers. A recent technology, xCELLigence Real Time Cell Analysis, is now allowing to monitor the cell migration in real time. This technology measures impedance changes caused by the gradual increase of electrode surface occupation by cells during the course of time and provide a Cell Index which is proportional to cellular morphology, spreading, ruffling and adhesion quality as well as cell number. In this paper we propose a macroscopic mathematical model, based on advection-reaction-diffusion partial differential equations, describing the cell migration assay using the real-time technology. We carried out numerical simulations to compare simulated model dynamics with data of observed biological experiments on three different cell lines and in two experimental settings: absence of chemotactic signals (basal migration) and presence of a chemoattractant. Overall we conclude that our minimal mathematical model is able to describe the phenomenon in the real time scale and numerical results show a good agreement with the experimental evidences.

A stochastic model for describing the firing activity of a couple of interacting neurons subject to time-dependent stimuli is proposed. Two stochastic differential equations suitably coupled and including periodic terms to represent stimuli imposed to one or both neurons are considered to describe the problem. We investigate the first passage time densities through specified firing thresholds for the involved time non-homogeneous Gauss-Markov processes. We provide simulation results and numerical approximations of the firing densities. Asymptotic behaviors of the first passage times are also given.

Aircraft measurements were used to estimate the CO2 emission rates of the city of Rome, assessed against high-resolution inventorial data. Three experimental flights were made, composed of vertical soundings to measure Planetary Boundary Layer (PBL) properties, and circular horizontal transects at various altitudes around the city area. City level emissions and associated uncertainties were computed by means of mass budgeting techniques, obtaining a positive net CO2 flux of 14.7±4.5, 2.5±1.2, and 10.3±1.2 ?mol m-2 s-1 for the three flights. Inventorial CO2 fluxes at the time of flights were computed by means of spatial and temporal disaggregation of the gross emission inventory, at 10.9±2.5, 9.6±1.3, and 17.4±9.6 ?mol m-2 s-1. The largest differences between the two dataset are associated with a greater variability of wind speed and direction in the boundary layer during measurements. Uncertainty partitioned into components related to horizontal boundary flows and top surface flow, revealed that the latter dominates total uncertainty in the presence of a wide variability of CO2 concentration in the free troposphere (up to 7 ppm), while it is a minor term with uniform tropospheric concentrations in the study area (within 2 ppm). Overall, we demonstrate how small aircraft may provide city level emission measurements that may integrate and validate emission inventories. Optimal atmospheric conditions and measurement strategies for the deployment of aircraft experimental flights are finally discussed.

Methods coming from statistics and pattern recognition to estimate the cloud mask from radiance measured by visible and infrared sensors on board satellites are gaining greater consideration for their ability to properly exploit the increasing number of channels available with current and next-generation sensors. Endowed with physical arguments, they give rise to robust methods for accurately estimating the cloud mask. Application of such classification methods to Moderate Resolution Imaging Spectroradiometer (MODIS) data is discussed in this paper. Three different types of MODIS datasets are considered: synthetic (radiance is simulated by proper radiative transfer models); annotated (real MODIS data labeled by a meteorologist as clear or cloudy); and real MODIS data, whose truth is obtained from the official MODIS cloud mask product. A full assessment of the MODIS spectral bands is performed, aimed at understanding the role of the spectral bands in detecting clouds and at achieving top performance with very few properly chosen spectral channels. Local methods that use spatial correlation of images to improve classification, reducing the pseudonuisance of nonlocal methods, have also been tested on real data.