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  1. Article ; Online: An Explainable Host Genetic Severity Predictor Model for COVID-19 Patients

    Onoja, Anthony / Raimondi, Francesco / Nanni, Mirco

    medRxiv

    Abstract: Understanding the COVID-19 severity and why it differs significantly among patients is a thing of concern to the scientific community. The major contribution of this study arises from the use of a voting ensemble host genetic severity predictor (HGSP) ... ...

    Abstract Understanding the COVID-19 severity and why it differs significantly among patients is a thing of concern to the scientific community. The major contribution of this study arises from the use of a voting ensemble host genetic severity predictor (HGSP) model we developed by combining several state-of-the-art machine learning algorithms (decision tree-based models: Random Forest and XGBoost classifiers). These models were trained using a genetic Whole Exome Sequencing (WES) dataset and clinical covariates (age and gender) formulated from a 5-fold stratified cross-validation computational strategy to randomly split the dataset to overcome model instability. Our study validated the HGSP model based on the 18 features (i.e., 16 identified candidate genetic variants and 2 covariates). We provided post-hoc model explanations through the ExplainerDashboard - an open-source python library framework, allowing for deeper insight into the prediction results. We applied the Enrichr and OpenTarget genetics bioinformatic interactive tools to associate the genetic variants for plausible biological insights, and domain interpretations such as pathways, ontologies, and disease/drugs. Through an unsupervised clustering of the SHAP feature importance values, we visualized the complex genetic mechanisms. Our findings show that while age and gender mainly influence COVID-19 severity, a specific group of patients experiences severity due to complex genetic interactions.
    Keywords covid19
    Language English
    Publishing date 2023-03-08
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2023.03.06.23286869
    Database COVID19

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  2. Book ; Online: One-Shot Traffic Assignment with Forward-Looking Penalization

    Cornacchia, Giuliano / Nanni, Mirco / Pappalardo, Luca

    2023  

    Abstract: Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient ... ...

    Abstract Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient route assignments. This paper introduces METIS, a cooperative, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of METIS against state-of-the-art one-shot methods. Compared to the best baseline, METIS significantly reduces CO2 emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes METIS as a promising solution for optimizing TA and urban transportation systems.
    Keywords Computer Science - Multiagent Systems
    Publishing date 2023-06-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: A Bag of Receptive Fields for Time Series Extrinsic Predictions

    Spinnato, Francesco / Guidotti, Riccardo / Monreale, Anna / Nanni, Mirco

    2023  

    Abstract: High-dimensional time series data poses challenges due to its dynamic nature, varying lengths, and presence of missing values. This kind of data requires extensive preprocessing, limiting the applicability of existing Time Series Classification and Time ... ...

    Abstract High-dimensional time series data poses challenges due to its dynamic nature, varying lengths, and presence of missing values. This kind of data requires extensive preprocessing, limiting the applicability of existing Time Series Classification and Time Series Extrinsic Regression techniques. For this reason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporates notions from time series convolution and 1D-SAX to handle univariate and multivariate time series with varying lengths and missing values. We evaluate BORF on Time Series Classification and Time Series Extrinsic Regression tasks using the full UEA and UCR repositories, demonstrating its competitive performance against state-of-the-art methods. Finally, we outline how this representation can naturally provide saliency and feature-based explanations.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Publishing date 2023-11-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Gross polluters and vehicles' emissions reduction

    Böhm, Matteo / Nanni, Mirco / Pappalardo, Luca

    2021  

    Abstract: Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus ... ...

    Abstract Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. We use GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of private vehicles in three European cities. We find that the emissions across the vehicles and roads are well approximated by heavy-tailed distributions and thus discover the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study contributes to shaping the discussion on how to measure emissions with digital data.

    Comment: Version to be published in Nature Sustainability. Minor changes due to the last round of reviews
    Keywords Physics - Physics and Society ; Computer Science - Other Computer Science
    Subject code 629
    Publishing date 2021-04-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Ranking places in attributed temporal urban mobility networks.

    Nanni, Mirco / Tortosa, Leandro / Vicent, José F / Yeghikyan, Gevorg

    PloS one

    2020  Volume 15, Issue 10, Page(s) e0239319

    Abstract: Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the ... ...

    Abstract Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.
    MeSH term(s) Algorithms ; Geographic Information Systems ; Humans ; London ; Movement ; Rome ; Spatio-Temporal Analysis
    Language English
    Publishing date 2020-10-14
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0239319
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Article ; Online: Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

    Nanni, Mirco / Andrienko, Gennady / Barabási, Albert-László

    2020  

    Abstract: The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in ... ...

    Abstract The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively, voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates - if and when they want, for specific aims - with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.
    Keywords Corona ; 005 ; 006 ; 629
    Subject code 303
    Language English
    Publishing country de
    Document type Book ; Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: How Routing Strategies Impact Urban Emissions

    Cornacchia, Giuliano / Böhm, Matteo / Mauro, Giovanni / Nanni, Mirco / Pedreschi, Dino / Pappalardo, Luca

    2022  

    Abstract: Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., carbon dioxide emissions and population exposure to pollution) is ... ...

    Abstract Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., carbon dioxide emissions and population exposure to pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.
    Keywords Computer Science - Computers and Society ; Computer Science - Multiagent Systems
    Subject code 629
    Publishing date 2022-07-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Modeling Events and Interactions through Temporal Processes -- A Survey

    Liguori, Angelica / Caroprese, Luciano / Minici, Marco / Veloso, Bruno / Spinnato, Francesco / Nanni, Mirco / Manco, Giuseppe / Gama, Joao

    2023  

    Abstract: In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for ... ...

    Abstract In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characterize the literature on the topic. We define an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically review the existing approaches based based on deep learning. Finally, we analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects.
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Publishing date 2023-03-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks

    Yeghikyan, Gevorg / Opolka, Felix L. / Nanni, Mirco / Lepri, Bruno / Lio', Pietro

    2020  

    Abstract: A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the ... ...

    Abstract A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the different spatial, temporal, social, and economic factors influencing urban mobility flows. These flows, along with the influencing factors, can be modelled as attributed graphs with both node and edge features characterising locations in a city and the various types of relationships between them. In this paper, we address the problem of assessing origin-destination (OD) car flows between a location of interest and every other location in a city, given their features and the structural characteristics of the graph. We propose three neural network architectures, including graph neural networks (GNN), and conduct a systematic comparison between the proposed methods and state-of-the-art spatial interaction models, their modifications, and machine learning approaches. The objective of the paper is to address the practical problem of estimating potential flow between an urban development project location and other locations in the city, where the features of the project location are known in advance. We evaluate the performance of the models on a regression task using a custom data set of attributed car OD flows in London. We also visualise the model performance by showing the spatial distribution of flow residuals across London.

    Comment: 9 pages, 5 figures, to be published in the Proceedings of 2020 IEEE International Conference on Smart Computing (SMARTCOMP 2020)
    Keywords Computer Science - Social and Information Networks ; Physics - Physics and Society
    Subject code 380
    Publishing date 2020-04-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article: Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown

    Bonato, Pietro / Cintia, Paolo / Fabbri, Francesco / Fadda, Daniele / Giannotti, Fosca / Lopalco, Pier Luigi / Mazzilli, Sara / Nanni, Mirco / Pappalardo, Luca / Pedreschi, Dino / Penone, Francesco / Rinzivillo, Salvatore / Rossetti, Giulio / Savarese, Marcello / Tavoschi, Lara

    Abstract: Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements ... ...

    Abstract Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modelling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. We address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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