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.

In this paper we present a new multi-scale method for reproducing traffic flow which couples a first-order macroscopic model with a second-order microscopic model, avoiding any interface or boundary conditions between them. The multi-scale model is characterized by the fact that microscopic and macroscopic descriptions are not spatially separated. On the contrary, the macro-scale is always active while the micro-scale is activated only if needed by the traffic conditions. The Euler-Godunov scheme associated to the model is conservative and it is able to reproduce typical traffic phenomena like stop & go waves.

Background. The three terms "panic", "irrationality", and "herding" are ubiquitous in the crowd dynamics literature and have a strong influence on both modelling and management practices. The terms are also commonly shared between the scientific and nonscientific domains. The pervasiveness of the use of these terms is to the point where their underlying assumptions have often been treated as common knowledge by both experts and lay persons. Yet, at the same time, the literature on crowd dynamics presents ample debate, contradiction, and inconsistency on these topics. Method. This review is the first to systematically revisit these three terms in a unified study to highlight the scope of this debate. We extracted from peer-reviewed journal articles direct quotes that offer a definition, conceptualisation, or supporting/contradicting evidence on these terms and/or their underlying theories. To further examine the suitability of the term herding, a secondary and more detailed analysis is also conducted on studies that have specifically investigated this phenomenon in empirical settings. Results. The review shows that (i) there is no consensus on the definition for the terms panic and irrationality and that (ii) the literature is highly divided along discipline lines on how accurate these theories/terminologies are for describing human escape behaviour. The review reveals a complete division and disconnection between studies published by social scientists and those from the physical science domain and also between studies whose main focus is on numerical simulation versus those with empirical focus. (iii) Despite the ambiguity of the definitions and the missing consensus in the literature, these terms are still increasingly and persistently mentioned in crowd evacuation studies. (iv) Different to panic and irrationality, there is relative consistency in definitions of the term herding, with the term usually being associated with '(blind) imitation'. However, based on the findings of empirical studies, we argue why, despite the relative consistency in meaning, (v) the term herding itself lacks adequate nuance and accuracy for describing the role of 'social influence' in escape behaviour. Our conclusions also emphasise the importance of distinguishing between the social influence on various aspects of evacuation behaviour and avoiding generalisation across various behavioural layers. Conclusions. We argue that the use of these three terms in the scientific literature does not contribute constructively to extending the knowledge or to improving the modelling capabilities in the field of crowd dynamics. This is largely due to the ambiguity of these terms, the overly simplistic nature of their assumptions, or the fact that the theories they represent are not readily verifiable. Recommendations. We suggest that it would be beneficial for advancing this research field that the phenomena related to these three terms are clearly defined by more tangible and quantifiable terms and be formulated as verifiable hypotheses, so they can be operationalized for empirical testing.

The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset

The properties of a semiflexible polymer with fixed ends exposed to oscillatory shear flow are investigated by simulations. The two-dimensionally confined polymer is modeled as a linear bead-spring chain, and the interaction with the fluid is described by the Brownian multiparticle collision dynamics approach. For small shear rates, the tethering of the ends leads to a more-or-less linear oscillatory response. However, at high shear rates, we found a strongly nonlinear reaction, with a polymer (partially) wrapped around the fixation points. This leads to an overall shrinkage of the polymer. Dynamically, the location probability of the polymer center-of-mass position is largest on a spatial curve resembling a limacon, although with an inhomogeneous distribution. We found shear-induced modifications of the normal-mode correlation functions, with a frequency doubling at high shear rates. Interestingly, an even-odd asymmetry for the Cartesian components of the correlation functions appears, with rather similar spectra for odd x- and even y-modes and vice versa. Overall, our simulations yielded an intriguing nonlinear behavior of tethered semiflexible polymers under oscillatory shear flow.

In the wide scenario of the optimization techniques, a large number of algorithms are inspired by natural processes, in many different ways. One of the latest is the Imperialist Competitive Algorithm (ICA) Atashpaz-Gargari and Lucas (2007), judged by their authors as very efficient and competitive with other popular optimization algorithms. However, its diffusion is still limited, so that it has not yet been adequately studied. In this paper, we have investigated the convergence properties of the ICA algorithm, observing the effect of the various coefficients and their role in the global convergence. Some modifications, including the coupling with a local search method, have been listed/suggested and then tested on a suite of standard algebraic test functions, verifying the improvements on the speed of convergence of the original algorithm. An application to naval design has been also included, in order to check the ability to solve realistic problems.

Particle Swarm Optimization is an evolutionary optimization algorithm, largely studied during the years: analysis of convergence, determination of the optimal coefficients, hybridization of the original algorithm and also the determination of the best relationship structure between the swarm elements (topology) have been investigated largely. Unfortunately, all these studies have been produced separately, and the same coefficients, derived for the original topology of the algorithm, have been always applied. The intent of this paper is to identify the best set of coefficients for different topological structures. A large suite of objective functions are considered and the best compromise coefficients are identified for each topology. Results are finally compared on the base of a practical ship design application.

The role of comics in science communications has been subject of a number of recent papers. While some debate on an accepted definition of what constitutes a science comics is still ongoing, the role of comics in science outreach is now universally recognized. [1] Here we present the Comics&Science comic books published by CNR Edizioni and edited by Roberto Natalini and Andrea Plazzi. The Comics&Science concept was implemented in the first place as a section of the Lucca Comics&Games festival, followed by the printed series in 2013. Comics&Science's philosophy is to have science elements, ideas and "bits" integrated into cartoons by the best professional writers and artists. The story must be fun, entertaining and overall artistically and aesthetically significant. Each issue is completed by articles and pieces about and around the topics touched by the story. A Comics&Science issue almost always starts with the cartoonist visiting a research lab/facility. Tipically, the artist has little or no formal scientific background and up to now this has proved to be an effective starting point for exchanging opinions and a cross-debate of sorts. Following the Comics&Science "protocol", Italian hugely popular cartoonist Zerocalcare (millions of books sold in the last few years) visited the Elettra and Fermi light sources prior to working to his "Light Issue", co-edited by the Istituto di Struttura della Materia-CNR (ISM- CNR) and Elettra Sincrotrone Trieste. The upcoming Comics&Science issue (Fall 2019) will be featuring a Periodic Table-inspired story by writer Giovanni Eccher and artist Sergio Ponchione, as a joint venture between the Department of Chemical Science and Technology of Materials (CNR-DSCTM) and the Young Chemists Group and the Group for the Diffusion of the Chemistry Culture of the Italian Chemical Society.

In this paper, an adaptive method for copy-move forgery detection and localization in digital images is proposed. The method employs wavelet transform with non constant Q factor and characterizes image pixels through the multiscale behavior of corresponding wavelet coefficients. The detection of forged regions is then performed by considering similar those pixels having the same multiscale behavior. The method is pointwise and the length of pixel features vector is image dependent, allowing for a more precise and fast detection of forged regions. The qualitative and quantitative evaluation of the experimental results reveals that the proposed method outperforms some existing transform-based methods in terms of performance and execution time.

The improvement of the readability of time-frequency transforms is an important topic in the field of fast-oscillating signal processing. The reassignment method is often used due to its adaptivity to different transforms and nice formal properties. However, it strongly depends on the selection of the analysis window and it requires the computation of the same transform using three different but well-defined windows. The aim of this work is to provide a simple method for spectrogram reassignment, named FIRST (Fast Iterative and Robust Reassignment Thinning), with comparable or better precision than classical reassignment method, a reduced computational effort, and a near independence of the adopted analysis window. To this aim, the time-frequency evolution of a multicomponent signal is formally provided and, based on this law, only a subset of time-frequency points is used to improve spectrogram readability. Those points are the ones less influenced by interfering components. Preliminary results show that the proposed method can efficiently reassign spectrograms more accurately than the classical method in the case of interfering signal components, with a significant gain in terms of required computational effort.