Global mean sea level rise associated with global warming has a major impact on coastal areas and represents one of the significant natural hazards. The Asia-Pacific region, which has the highest concentration of human population in the world, represents one of the larger areas on Earth being threatened by the rise of sea level. Recent studies indicate a global sea level of 3.2 mm/yr as measured from 20 years of satellite altimetry. The combined effect of sea level rise and local land subsidence, can be overwhelming for coastal areas. The Synthetic Aperture Radar (SAR) interferometry technique is used to process a time series of TerraSAR-X images and estimate the land subsidence in the urban area of Singapore. Interferometric SAR (InSAR) measurements are merged to the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 sea-level rise scenarios to identify projected inundated areas and provide a map of flood vulnerability. Subsiding rates larger than 5 mm/year are found near the shore on the low flat land, associated to areas recently reclaimed or built. The projected flooded map of Singapore are provided for different sea-level rise scenarios. In this study, we show that local land subsidence can increase the flood vulnerability caused by sea level rise by 2100 projections. This can represent an increase of 25% in the flood area in the central area of Singapore for the RCP4.5 scenario.

In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts.

We investigate the influence of the perturbed (by a solar X-ray flare) ionospheric D-region on the global navigation satellite systems (GNSS) and synthetic aperture radar (SAR) signals. We calculate a signal delay in the D-region based on the low ionospheric monitoring by very-low-frequency (VLF) radio waves. The results show that the ionospheric delay in the perturbed D-region can be important and, therefore, should be taken into account in modeling the ionospheric influence on the GNSS and SAR signal propagation and in calculations relevant for space geodesy. This conclusion is significant because numerous existing models ignore the impact of this ionospheric part on the GNSS and SAR signals due to its small electron density which is true only in quiet conditions and can result in significant errors in space geodesy during intensive ionospheric disturbances.

Online social networks are nowadays one of the most effective and widespread tools used to share information. In addition to being employed by individuals for communicating with friends and acquaintances, and by brands for marketing and customer service purposes, they constitute a primary source of daily news for a significant number of users. Unfortunately, besides legit news, social networks also allow to effectively spread inaccurate or even entirely fabricated ones. Also due to sensationalist claims, misinformation can spread from the original sources to a large number of users in a very short time, with negative consequences that, in extreme cases, can even put at risk public safety or health. In this work we discuss and propose methods to limit the spread of misinformation over online social networks. The issue is split in two separate sub-problems. We first aim to identify the most probable sources of the misinformation among the subset of users that have been reached by it. In the second step, assuming to know the misinformation sources, we want to locate a minimum number of monitors (that is, entities able to identify and block false information) in the network in order to prevent that the misinformation campaign reaches some "critical" nodes while maintaining low the number of nodes exposed to the infection. For each of the two issues, we provide both heuristics and mixed integer programming formulations. To verify the quality and efficiency of our suggested solutions, we conduct experiments on several real-world networks. The results of this extensive experimental phase validate our heuristics as effective tools to contrast the spread of misinformation in online social networks. Regarding the source identification step, our approach showed success rates above 80% in most of the considered settings, and above 60% in almost all of them. With respect to the second issue, our heuristic proved to be able to obtain solutions that exceeded (in terms of number of required monitors) the ones obtained through our MILP-based approach of more than 20% in only few test scenarios. Our heuristics for both problems also proved to outperform significantly some previously proposed algorithms.

In this paper we introduce and study the Knapsack Problem with Forfeits. With respect to the classical definition of the problem, we are given a collection of pairs of items, such that the inclusion of both in the solution involves a reduction of the profit. We propose a mathematical formulation and two heuristic algorithms for the problem. Computational results validate the effectiveness of our approaches.

Science et BD, comme sauvagarder la rigueur en situations extrêmes

L'Intelligenza Artificiale nasce come disciplina negli anni Cinquanta e negli ultimi anni ha avuto una vera e propria esplosione: nei cellulari, nei computer, nell'analisi delle immagini biomediche e nel riconoscimento del linguaggio naturale, tra le tantissime applicazioni. Ma come si è arrivati a questo? Cosa ci si aspetta? Questa storia di Diego Cajelli disegnata da Andrea Scoppetta è nata grazie alla collaborazione con l'AIxIA (Associazione Italiana per l'Intelligenza Artificiale) e ci parla di N3well. Che è anche una macchina.

Impossibile sopravvalutare l'importanza di Leonardo Pisano, detto "il Fibonacci". Agli inizi del XIII secolo il suo Liber Abbaci porta in Occidente i numeri indo-arabi e la notazione posizionale che usiamo ancora oggi. Descrive inoltre numerosi problemi pratici di grande importanza per i mercanti dell'epoca, spiegando come risolverli con "i nuovi numeri" e i raffinati metodi di calcolo (oggi diremmo "algoritmi") da lui importati. Negli 850 anni dalla nascita, Comics&Science lo ricorda in collaborazione con il Museo degli Strumenti per il Calcolo, l'Università di Pisa, e naturalmente con Il libro di Leonardo, la storia a fumetti che Claudia Flandoli ambienta nella Pisa dell'epoca che accoglie il suo Figliol Prodigo, carico di entusiamo e nuove conoscenze.

SARS-CoV-2 is highly contagious, rapidly turned into a pandemic, and is causing a relevant number of critical to severe life-threatening COVID-19 patients. However, robust statistical studies of a large cohort of patients, potentially useful to implement a vaccination campaign, are rare. We analyzed public data of about 19,000 patients for the period 28 February to 15 May 2020 by several mathematical methods. Precisely, we describe the COVID-19 evolution of a number of variables that include age, gender, patient's care location, and comorbidities. It prompts consideration of special preventive and therapeutic measures for subjects more prone to developing life-threatening conditions while affording quantitative parameters for predicting the effects of an outburst of the pandemic on public health structures and facilities adopted in response. We propose a mathematical way to use these results as a powerful tool to face the pandemic and implement a mass vaccination campaign. This is done by means of priority criteria based on the influence of the considered variables on the probability of both death and infection.

We report on the Covid-19 epidemic in Italy in relation to the extraordinary measures implemented by the Italian Government between the 24th of February and the 12th of March. We analysed the Covid-19 cumulative incidence (CI) using data from the 1st to the 31st of March. We estimated that in Lombardy, the worst hit region in Italy, the observed Covid-19 CI diverged towards values lower than the ones expected in the absence of government measures approximately 7-10 days after the measures implementation. The Covid-19 CI growth rate peaked in Lombardy the 22nd of March and in other regions between the 24th and the 27th of March. The CI growth rate peaked in 87 out of 107 Italian provinces on average 13.6 days after the measures implementation. We projected that the CI growth rate in Lombardy should substantially slow by mid-May 2020. Other regions should follow a similar pattern. Our projections assume that the government measures will remain in place during this period. The evolution of the epidemic in different Italian regions suggests that the earlier the measures were taken in relation to the stage of the epidemic, the lower the total cumulative incidence achieved during this epidemic wave. Our analyses suggest that the government measures slowed and eventually reduced the Covid-19 CI growth where the epidemic had already reached high levels by mid-March (Lombardy, Emilia-Romagna and Veneto) and prevented the rise of the epidemic in regions of central and southern Italy where the epidemic was at an earlier stage in mid-March to reach the high levels already present in northern regions. As several governments indicate that their aim is to "push down" the epidemic curve, the evolution of the epidemic in Italy supports the WHO recommendation that strict containment measures should be introduced as early as possible in the epidemic curve.

In this paper we establish the higher differentiability of solutions to the Dirichlet problem {div(A(x,Du))+b(x)u(x)=fin?u=0on??under a Sobolev assumption on the partial map x-> A(x, ?). The novelty here is that we take advantage from the regularizing effect of the lower order term to deal with bounded solutions.

Graphical models are well-known mathematical objects for describing conditional dependency relationships between random variables of a complex system. Gaussian graphical models refer to the case of multivariate Gaussian variable for which the graphical model is encoded through the support of corresponding inverse covariance (precision) matrix. We consider a problem of estimating multiple Gaussian graphical models from high- dimensional data sets under the assumption that they share the same conditional independence structure. However, the individual correlation matrices can differ. Such a problem can be motivated by applications where data comes from different sources and can be collected in distinct classes or groups. We propose a joint data estimation that uses a node-wise penalized regression approach. Grouped Lasso penalty simultaneously guarantees the resulting adjacency matrix's symmetry and the joint learning of the graphs. We solve the minimization problem using the group descent algorithm and establish the proposed solution's consistency and sparsity properties. Finally, we show how the regularization parameter can be estimated using cross-validation and BIC. We provide a novel R package jewel with the implementation of the proposed method and illustrate our estimator's performance through simulated and real data examples. We compare the proposed approach with other available alternatives.

Questo documento riassume l'attività svolta nei vari WP, le azioni completate e lo stato di del progetto per il periodo di attività dal 19 febbraio 2020 (RA1) al 16 giugno 2020 (RA2).

The main goal of this work package is to establish a convention for the interfaces files, both between the various modules which compose the final retrieval system, and the input/output files.

Mid-infrared signals in the 2–5 μm wavelength range have been transmitted through samples of polymer pipes, as commonly used in the water supply industry. It is shown that simple through-transmission images can be obtained using a broad spectrum source and a suitable camera. This leads to the possibility of tomography, where images are obtained as the measurement system is rotated with respect to the axis of the pipe. The unusual 3D geometry created by a source of finite size and the imaging plane of a camera, plus the fact that refraction at the pipe wall would cause significant ray bending, meant that the reconstruction of tomographic images had to be considered with some care. A result is shown for a thinning defect on the inner wall of a polymer water pipe, demonstrating that such changes can be reconstructed successfully.

In piecewise smooth dynamical systems, a co-dimension 2 discontinuity manifold can be attractive either through partial sliding or by spiraling. In this work we prove that both attractivity regimes can be analyzed by means of the moments solution, a spiraling bifurcation parameter and a novel attractivity parameter, which changes sign when attractivity switches from sliding to spiraling attractivity or vice-versa. We also study what happens at what we call attractivity transition points, showing that the spiraling bifurcation parameter is always zero at those points.

Chandipura vesiculovirus (CHPV) is a rapidly emerging pathogen responsible for causing acute encephalitis. Due to its widespread occurrence in Asian and African countries, this has become a global threat, and there is an urgent need to design an effective and nonallergenic vaccine against this pathogen. The present study aimed to develop a multi-epitope vaccine using an immunoinformatics approach. The conventional method of vaccine design involves large proteins or whole organism which leads to unnecessary antigenic load with increased chances of allergenic reactions. In addition, the process is also very time-consuming and labor-intensive. These limitations can be overcome by peptide-based vaccines comprising short immunogenic peptide fragments that can elicit highly targeted immune responses, avoiding the chances of allergenic reactions, in a relatively shorter time span. The multi-epitope vaccine constructed using CTL, HTL, and IFN-γ epitopes was able to elicit specific immune responses when exposed to the pathogen, in silico. Not only that, molecular docking and molecular dynamics simulation studies confirmed a stable interaction of the vaccine with the immune receptors. Several physicochemical analyses of the designed vaccine candidate confirmed it to be highly immunogenic and nonallergic. The computer-aided analysis performed in this study suggests that the designed multi-epitope vaccine can elicit specific immune responses and can be a potential candidate against CHPV.

Thermal convection is ubiquitous in nature as well as in many industrial applications. The identification of effective control strategies to, e.g. suppress or enhance the convective heat exchange under fixed external thermal gradients is an outstanding fundamental and technological issue. In this work, we explore a novel approach, based on a state-of-the-art Reinforcement Learning (RL) algorithm, which is capable of significantly reducing the heat transport in a two-dimensional Rayleigh–Bénard system by applying small temperature fluctuations to the lower boundary of the system. By using numerical simulations, we show that our RL-based control is able to stabilise the conductive regime and bring the onset of convection up to a Rayleigh number (Formula presented.), whereas state-of-the-art linear controllers have (Formula presented.). Additionally, for (Formula presented.), our approach outperforms other state-of-the-art control algorithms reducing the heat flux by a factor of about 2.5. In the last part of the manuscript, we address theoretical limits connected to controlling an unstable and chaotic dynamics as the one considered here. We show that controllability is hindered by observability and/or capabilities of actuating actions, which can be quantified in terms of characteristic time delays. When these delays become comparable with the Lyapunov time of the system, control becomes impossible.

Physical distancing, as a measure to contain the spreading of Covid-19, is defining a “new normal”. Unless belonging to a family, pedestrians in shared spaces are asked to observe a minimal (country-dependent) pairwise distance. Coherently, managers of public spaces may be tasked with the enforcement or monitoring of this constraint. As privacy-respectful real-time tracking of pedestrian dynamics in public spaces is a growing reality, it is natural to leverage on these tools to analyze the adherence to physical distancing and compare the effectiveness of crowd management measurements. Typical questions are: “in which conditions non-family members infringed social distancing?”, “Are there repeated offenders?”, and “How are new crowd management measures performing?”. Notably, dealing with large crowds, e.g. in train stations, gets rapidly computationally challenging. In this work we have a two-fold aim: first, we propose an efficient and scalable analysis framework to process, offline or in real-time, pedestrian tracking data via a sparse graph. The framework tackles efficiently all the questions mentioned above, representing pedestrian-pedestrian interactions via vector-weighted graph connections. On this basis, we can disentangle distance offenders and family members in a privacy-compliant way. Second, we present a thorough analysis of mutual distances and exposure-times in a Dutch train platform, comparing pre-Covid and current data via physics observables as Radial Distribution Functions. The versatility and simplicity of this approach, developed to analyze crowd management measures in public transport facilities, enable to tackle issues beyond physical distancing, for instance the privacy-respectful detection of groups and the analysis of their motion patterns.

The genome versus experience dichotomy has dominated understanding of behavioral individuality. By contrast, the role of nonheritable noise during brain development in behavioral variation is understudied. Using Drosophila melanogaster, we demonstrate a link between stochastic variation in brain wiring and behavioral individuality. A visual system circuit called the dorsal cluster neurons (DCN) shows nonheritable, interindividual variation in right/left wiring asymmetry and controls object orientation in freely walking flies. We show that DCN wiring asymmetry instructs an individual's object responses: The greater the asymmetry, the better the individual orients toward a visual object. Silencing DCNs abolishes correlations between anatomy and behavior, whereas inducing DCN asymmetry suffices to improve object responses.