The evolution of a geothermal system is studied. A mathematical model is proposed and the corresponding free boundary problem is formulated in a one-dimensional geometry. A situation corresponding to the geothermal field in Larderello, Tuscany (Italy) is considered, showing that the problem has two characteristic time scales, related to the motion of interface and diffusion of vapor. Since the former is much slower, a quasi-steady approximation can be introduced and solved, obtaining a qualitative description of the evolution of the Larderello basin from a liquid-dominated to a vapor-dominated situation. This is in agreement with the geological results.

Dinur and Shamir's cube attack has attracted significant attention in the literature. Nevertheless, the lack of implementations achieving effective results casts doubts on its practical relevance. On the theoretical side, promising results have been recently achieved leveraging on division trails. The present paper follows a more practical approach and aims at giving new impetus to this line of research by means of a cipher-independent flexible framework that is able to carry out the cube attack on GPU/CPU clusters. We address all issues posed by a GPU implementation, providing evidence in support of parallel variants of the attack and identifying viable directions for solving open problems in the future. We report the results of running our GPU-based cube attack against round-reduced versions of three well-known ciphers: Trivium, Grain-128 and SNOW 3G. Our attack against Trivium improves the state of the art, permitting full key recovery for Trivium reduced to (up to) 781 initialization rounds (out of 1152) and finding the first-ever maxterm after 800 rounds. In this paper, we also present the first standard cube attack (i.e., neither dynamic nor tester) to yield maxterms for Grain-128 up to 160 initialization rounds on non-programmable hardware. We include a thorough evaluation of the impact of system parameters and GPU architecture on the performance. Moreover, we demonstrate the scalability of our solution on multi-GPU systems. We believe that our extensive set of results can be useful for the cryptographic engineering community at large and can pave the way to further results in the area.

We consider a nonlinear system of partial differential equations which describes the dynamics of two types of cell densities with contact inhibition. After a change of variables the system turns out to be parabolic-hyperbolic and admits travelling wave solutions which solve a 3D dynamical system. Compared to the scalar Fisher-KPP equation, the structure of the travelling wave solutions is surprisingly rich and to unravel part of it is the aim of the present paper. In particular, we consider a parameter regime where the minimal wave velocity of the travelling wave solutions is negative. We show that there exists a branch of travelling wave solutions for any nonnegative wave velocity, which is not connected to the travelling wave solution with minimal wave velocity. The travelling wave solutions with nonnegative wave velocity are strictly positive, while the solution with minimal one is segregated in the sense that the product uv vanishes.

We introduce and analyze a new, nonlinear fourth-order regularization of forwardbackward parabolic equations. In one space dimension, under general assumptions on the potentials, which include those of Perona-Malik type, we prove existence of Radon measure-valued solutions under both natural and essential boundary conditions. If the decay at infinity of the nonlinearities is sufficiently fast, we also exhibit examples of local solutions whose atomic part arises and/or persists (in contrast to the linear fourth-order regularization) and even disappears within finite time (in contrast to pseudoparabolic regularizations).

-- We prove existence and uniqueness of Radon measure-valued solutions of the Cauchy problem for a first order scalar hyperbolic conservation law in one space dimension, the initial data being a finite superposition of Dirac masses and the flux being Lipschitz continuous, bounded and suciently smooth. The novelty of the paper is the introduction of a compatibility condition which, combined with standard entropy conditions, guarantees uniqueness.

L'idea imprenditoriale da cui prende origine la start-up ProNeuro, nasce come conseguenza del lavoro di ricerca svolto dai soci fondatori presso il Consiglio Nazionale delle Ricerche (CNR). Questo lavoro ha portato negli ultimi 3 anni al deposito di due domande di brevetto italiano, di cui una già estesa in PCT, che proteggono l'utilizzo della molecola ProNGF-A per scopi terapeutici mirati alla cura di patologie neurologiche e infiammatorie (domanda di brevetto Nr. 102018000003279 del 05/03/2018 e PCT/IB2019/051753 del 05/03/2019) e la produzione di una forma mutata di ProNGF-A e il suo utilizzo per terapia neurologica e di patologie cutanee (domanda di brevetto numero 102019000014646 del 12/08/2019). Tali brevetti sono di proprietà del CNR, mentre ProNeuro ha messo a punto un sistema di offerta finalizzato alla loro valorizzazione. Attraverso attività di Ricerca e Sviluppo, ProNeuro individua principi attivi farmacologici con attività protettiva e riparativa per il sistema nervoso, ne modifica la struttura per renderli maggiormente efficaci, sicuri e biocompatibili, mette a punto i metodi produttivi ed esegue le prime fasi di caratterizzazione dei loro effetti, prima di proporli ad aziende farmaceutiche per un successivo sviluppo come farmaci destinati al mercato. ProNeuro commercializza, quindi, i diritti di utilizzo della proprietà intellettuale e una serie di prodotti collegati alle attività di discovery, produzione (trasferimento tecnologico) e prima validazione sia predittiva che biologica di nuovi neurofarmaci. ProNeuro avrà la forma giuridica di Società a responsabilità limitata e si configura come spin-off CNR. Come tale, il rapporto tra la società ProNeuro e il CNR è regolato dal "Regolamento per la costituzione e la partecipazione del CNR alle Imprese Spin off, Del,18/2019". I brevetti sopracitati, attualmente di proprietà del CNR, verranno concessi in licenza a ProNeuro, con possibilità di sub-licenziare a terzi, sulla base del suddetto Regolamento. Questo prevede, infatti, la cessione a condizioni agevolate delle licenze sui brevetti di proprietà CNR, la messa a disposizione di risorse logistiche e strumentali in fase di start-up e l'autorizzazione al proprio personale a svolgere attività a favore delle spin-off, con copertura dei costi salariali per un terzo del tempo lavorativo per tre anni. La sede dell'impresa è stata individuata presso l'Istituto di Farmacologia Traslazionale del CNR, via del Fosso del Cavaliere 100, 00133 Roma

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.

The properties of a semiflexible polymer with fixed ends under oscillatory shear flow are investigated by numerical simulations. The polymer is confined in two dimensions and is modeled as a worm-like chain. The interaction with the fluid is taken into account by the Brownian multiparticle collision dynamics approach. For small shear rates, a linear oscillatory response appears. However, at high shear rates, we find a strongly nonlinear behavior with the polymer wrapping around the fixation points and shrinking. The polymer center of mass is distributed on a spatial curve resembling a lima\c{c}on with an inhomogeneous distribution. Normal-mode correlation functions are changed by shear and a frequency doubling is observed at high shear rates. An even-odd asymmetry for the Cartesian components of the correlation functions is found with rather similar spectra for odd $x$- and even $y$-modes and vice versa. Our study yields an interesting nonlinear behavior of anchored semiflexible polymers under oscillatory shear flow. Preliminary results for the case of a semiflexible polymer with one fixed end exposed to oscillatory shear will be also provided.

We consider the Erdös-Rényi random graph G(n,p) and we analyze the simple irreversible epidemic process on the graph, known in the literature as bootstrap percolation. We give a quantitative version of some results by Janson et al. (2012), providing a fine asymptotic analysis of the final size A_n of active nodes, under a suitable super-critical regime. More specifically, we establish large deviation principles for the sequence of random variables n-A_n/f (n) with explicit rate functions and allowing the scaling function f to vary in the widest possible range.

In recent years, a number of functional inequalities have been derived for Poisson random measures, with a wide range of applications. In this paper, we prove that such inequalities can be extended to the setting of marked temporal point processes, under mild assumptions on their Papangelou conditional intensity. First, we derive a Poincare inequality. Second, we prove two transportation cost inequalities. The first one refers to functionals of marked point processes with a Papangelou conditional intensity and is new even in the setting of Poisson random measures. The second one refers to the law of marked temporal point processes with a Papangelou conditional intensity, and extends a related inequality which is known to hold on a general Poisson space. Finally, we provide a variational representation of the Laplace transform of functionals of marked point processes with a Papangelou conditional intensity. The proofs make use of an extension of the Clark-Ocone formula to marked temporal point processes. Our results are shown to apply to classes of renewal, nonlinear Hawkes and Cox point processes.

Abstract inviato a Conferenza Internazionale "Statistical Mechanics and Field Theory 2019" - Bari 11-13/12/2019

Edge and Fog Computing will be increasingly pervasive in the years to come due to the benefits they bring in many specific use-case scenarios over traditional Cloud Computing. Nevertheless, the security concerns Fog and Edge Computing bring in have not been fully considered and addressed so far, especially when considering the underlying technologies (e.g. virtualization) instrumental to reap the benefits of the adoption of the Edge paradigm. In particular, these virtualization technologies (i.e. Containers, Real Time Operating Systems, and Unikernels), are far from being adequately resilient and secure. Aiming at shedding some light on current technology limitations, and providing hints on future research security issues and technology development, in this paper we introduce the main technologies supporting the Edge paradigm, survey existing issues, introduce relevant scenarios, and discusses benefits and caveats of the different existing solutions in the above introduced scenarios. Finally, we provide a discussion on the current security issues in the introduced context, and strive to outline future research directions in both security and technology development in a number of Edge/Fog scenarios.

Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type-2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients. In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statis- tical model able to recapitulate the simulated dynamics. The resulting machine learning model adequately predicts the synthetic data and can therefore be used as a computationally- cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self assessment by informed and aware individuals.

Presentazione delle attività di ricerca su computational immunology e network medicine al workshop "Hot Topics in Systems" all'interno della conferenza PLACE2019

The IMACS Series in Computational and Applied Mathematics collects refereed papers on research results presented in scientific events held under the auspices of the International Association for Mathematics and Computers in Simulation (IMACS). The MASCOT2018 Proceedings Book refers to the 15th IMACS/ISGG International Workshop of the MASCOT series of meetings which is yearly organised since 2001. The latest MASCOT 2018, 15th Meeting on Applied Scientific Computing and Tools, Grid Generation, Approximation and Visualization, dealt with mathematical modelling, methodologies and advanced applications, which are the themes of the contributions here collected.

We developed a Self-Adapting Constraint Retrieval Scheme (SACRS) to retrieve ozone profiles from nadir infrared satellite measurements. In this algorithm, the constraint is variable in altitude and adapted automatically for each individual measurement. The algorithm is tested on synthetic observations representing the future IASI-NG satellite observations and considering either ozonesonde measurements or chemistry-transport model ozone simulations to represent the true ozone (pseudo-reality). The ozone retrievals are evaluated mainly for the troposphere with a specific focus on the lower troposphere between the surface and 6 km. Compared to a previous algorithm based on a fixed constraint retrieval scheme (FCRS), the biases, correlation and error estimates are improved with the SACRS. The bias is reduced by 40% and the correlation coefficient increases from 0.72 to 0.80. The SACRS algorithm also leads to an enhanced sensitivity in the lower troposphere with degrees of freedom for signal up to 0.83, increased by 11% compared to the FCRS. The SACRS performs especially well where current algorithms usually fail, namely for polar and tropical air masses. The bias is reduced from 8.6% to 0.5% in the troposphere (surface-9 km) when considering polar cases and from 24.4% to 10.1% in the upper troposphere - lower troposphere column (12-18 km) in the tropics.

This paper studies a scheduling problem application for the optimization of the employees used in aircrafts' refueling in a medium size airport. The problem is modelled as a particular resource leveling problem for which we provide a mixed integer mathematical formulation that we solve with CPLEX. The model allows to evaluate and analyse different scenarios that could be considered by the company in place of the current one in order to rearrange the available human resources used in refueling activity. Experimental results on a set of real test cases provided by an oil & gas company are discussed.

The process of aircraft refueling has crucial impact in the performance of an airport. It is in fact of common knowledge that one of the most important indicators for benchmarking an airport is the punctuality of flights departure. To assure high results, the airplane service activities such as passengers boarding, baggage handling and aircraft refueling must not delay one another and the overall departure time. The scope of the proposed study is to produce an instrument capable of simulating the process of the aircraft refueling in the airport environment and to consider different scenarios and evaluate their impact in the overall performance. This tool has significant relevance for the company whom process we have analyzed, allowing it to be able also to evaluate easily and in a short period of time complex changes in the process.

Many scientific applications require the solution of large and sparse linear systems of equations using Krylov subspace methods; in this case, the choice of an effective preconditioner may be crucial for the convergence of the Krylov solver. Algebraic MultiGrid (AMG) methods are widely used as preconditioners, because of their optimal computational cost and their algorithmic scalability. The wide availability of GPUs, now found in many of the fastest supercomputers, poses the problem of implementing efficiently these methods on high-throughput processors. In this work we focus on the application phase of AMG preconditioners, and in particular on the choice and implementation of smoothers and coarsest-level solvers capable of exploiting the computational power of clusters of GPUs. We consider block-Jacobi smoothers using sparse approximate inverses in the solve phase associated with the local blocks. The choice of approximate inverses instead of sparse matrix factorizations is driven by the large amount of parallelism exposed by the matrix-vector product as compared to the solution of large triangular systems on GPUs. The selected smoothers and solvers are implemented within the AMG preconditioning framework provided by the MLD2P4 library, using suitable sparse matrix data structures from the PSBLAS library. Their behaviour is illustrated in terms of execution speed and scalability, on a test case concerning groundwater modelling, provided by the Julich Supercomputing Center within the Horizon 2020 Project EoCoE.

Graph Laplacian is a popular tool for analyzing graphs, in particular in graph partitioning and clustering. Given a notion of similarity (via an adjacency matrix), graph clustering refers to identifying different groups such that vertices in the same group are more similar compared to vertices across different groups. Data clustering can be reformulated in terms of a graph clustering problem when the given set of data is represented as a graph, also known as similarity graph. In this context, eigenvectors of the graph Laplacian are often used to obtain a new geometric representation of the original data set which generally enhances cluster properties and improves cluster detection. In this work, we apply a bootstrap Algebraic MultiGrid (AMG) method which constructs a set of vectors associated with the graph Laplacian. These vectors, referred to as algebraically smooth ones, span a low-dimensional euclidean space, which we use to represent the data, enabling cluster detection both in synthetic and in realistic well-clustered graphs. We show that in the case of a good quality bootstrap AMG, the computed smooth vectors employed in the construction of the final AMG operator, which by construction is spectrally equivalent to the originally given graph Laplacian, accurately approximate the space in the lower portion of the spectrum of the preconditioned operator. Thus, our approach can be viewed as a spectral clustering technique associated with the generalized spectral problem (Laplace operator versus the final AMG operator), and hence it can be seen as an extension of the classical spectral clustering which employs a standard eigenvalue problem.