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arXiv 2023年

作者： Bucarelli, Maria Sofia Larsen, Matilde Fjeldsø Schwiegelshohn, Chris Toftrup, Mads Bech Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy Department of Computer Science Aarhus University Denmark

Given a set of points, clustering consists of finding a partition of a point set into k clusters such that the center to which a point is assigned is as close as possible. Most commonly, centers are points themselves, which leads to the famous k-median and k-means objectives. One may also choose centers to be j dimensional subspaces, which gives rise to subspace clustering. In this paper, we consider learning bounds for these problems. That is, given a set of n samples P drawn independently from some unknown, but fixed distribution D, how quickly does a solution computed on P converge to the optimal clustering of D? We give several near optimal results. In particular, 1. For center-based objectives, we show a convergence rate of Õ (√k/n ) . This matches the known optimal bounds of [Fefferman, Mitter, and Narayanan, Journal of the Mathematical Society 2016] and [Bartlett, Linder, and Lugosi, IEEE Trans. Inf. Theory 1998] for k-means and extends it to other important objectives such as k-median. 2. For subspace clustering with j-dimensional subspaces, we show a convergence rate of Õ (√kj2/n ) . These are the first provable bounds for most of these problems. For the specific case of projective clustering, which generalizes k-means, we show a convergence rate of Ω (√kj/n ) is necessary, thereby proving that the bounds from [Fefferman, Mitter, and Narayanan, Journal of the Mathematical Society 2016] are essentially optimal. © 2023, CC BY.

关键词： K-means clustering

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Network-based analysis to uncover drug-induced adverse side-effects

Network-based analysis to uncover drug-induced adverse side-...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Funari, Alessio Paci, Paola Conte, Federica Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy Karolinska Institutet Stockholm17177 Sweden

ISBN: (纸本)9798350337488

Despite the drug approval process consists of extremely rigorous clinical and preclinical studies, not all side effects are identified before its marketing, posing a significant risk to public health. Furthermore, considering the huge use of economic and human resources, in-silico predictive approaches for the identification of side effects are essential. In this study, we introduce a new method based on random walk with restart algorithm to delineate previously unidentified links between drugs and side effects, and we apply it on the drug-induced Asthma and long QT syndrome. We identified the genes potentially involved in the development of the analyzed side effect by comparing side-effect-related drugs with drugs not known to induce side effects. Analyzing the sets of genes most likely influenced by the perturbation of each individual drug, we observed that, on average, side-effect-related drugs perturb a higher percentage of genes involved in the development of side effects compared to side-effect-unrelated drugs. Based on this finding, we developed a classifier to explore all possible unknown associations between drugs and side effects. This method can be extended to the analysis of other side effects as well. © 2023 IEEE.

关键词： network medicine random walk side effect

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Optimal Placement of Nature-Based Solutions for Urban Challenges

arXiv

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arXiv 2025年

作者： Pinto, Diego Maria Russo, Davide Donato Sudoso, antonio M. Institute for Systems Analysis and Computer Science "Antonio Ruberti" National Research Council Via dei Taurini 19 Rome00185 Italy Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Via Ariosto 25 Rome00185 Italy

Increased urbanization and climate change intensify urban heat islands and degrade air quality, making current mitigation strategies insufficient. Nature-based solutions (NBSs), such as parks, green walls, roofs, and street trees, offer a promising means to regulate urban temperatures and enhance air quality. However, determining their optimal placement to maximize environmental benefits remains a pressing challenge. Leveraging Operational Research (OR) tools, we propose a Mixed-Integer Linear Programming (MILP) model that integrates multiple factors, including urban challenges, physical constraints, clustering techniques, convolution theory, and fairness considerations. This model determines the optimal placement of NBSs by addressing metrics such as ground temperature, air quality, and accessibility to green spaces. Through several case study analyses, we demonstrate the effectiveness of our approach in improving environmental and social indicators. This research holds implications for policy and practice, empowering urban planners and policymakers to make informed decisions regarding NBS implementation. Such decisions ensure that investments in urban greening yield maximum environmental, social, and economic benefits. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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On generalization bounds for projective clustering 23

On generalization bounds for projective clustering

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Maria Sofia Bucarelli Matilde Fjeldsø Larsen Chris Schwiegelshohn Mads Bech Toftrup Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy Department of Computer Science Aarhus University Denmark

Given a set of points, clustering consists of finding a partition of a point set into k clusters such that the center to which a point is assigned is as close as possible. Most commonly, centers are points themselves, which leads to the famous k-median and k-means objectives. One may also choose centers to be j dimensional subspaces, which gives rise to subspace clustering. In this paper, we consider learning bounds for these problems. That is, given a set of n samples P drawn independently from some unknown, but fixed distribution Ɗ, how quickly does a solution computed on P converge to the optimal clustering of Ɗ? We give several near optimal results. In particular,1. For center-based objectives, we show a convergence rate of Õ(√k/n). This matches the known optimal bounds of [Fefferman, Mitter, and Narayanan, Journal of the Mathematical Society 2016] and [Bartlett, Linder, and Lugosi, IEEE Trans. Inf. Theory 1998] for k-means and extends it to other important objectives such as k-median.2. For subspace clustering with j -dimensional subspaces, we show a convergence rate of Õ(√kj2/n). These are the first provable bounds for most of these problems. For the specific case of projective clustering, which generalizes k-means, we show a convergence rate of Ω(√kj/n) is necessary, thereby proving that the bounds from [Fefferman, Mitter, and Narayanan, Journal of the Mathematical Society 2016] are essentially optimal.

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Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition

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Computing and Software for Big science 2023年第1期7卷 1-18页

作者： Maglianella, Leandro Nicoletti, Lorenzo Giagu, Stefano Napoli, Christian Scardapane, Simone Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Rome Italy Department of Physics Sapienza University of Rome Rome Italy Institute for Systems Analysis and Computer Science Italian National Research Council Rome Italy Department of Computational Intelligence Electronics and Telecommunications Czestochowa University of Technology Czestochowa Poland Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy

Neural networks are commonly defined as ‘black-box’ models, meaning that the mechanism describing how they give predictions and perform decisions is not immediately clear or even understandable by humans. Therefore, Explainable Artificial Intelligence (xAI) aims at overcoming such limitation by providing explanations to Machine Learning (ML) algorithms and, consequently, making their outcomes reliable for users. However, different xAI methods may provide different explanations, both from a quantitative and a qualitative point of view, and the heterogeneity of approaches makes it difficult for a domain expert to select and interpret their result. In this work, we consider this issue in the context of a high-energy physics (HEP) use-case concerning muonic motion. In particular, we explored an array of xAI methods based on different approaches, and we tested their capabilities in our use-case. As a result, we obtained an array of potentially easy-to-understand and human-readable explanations of models’ predictions, and for each of them we describe strengths and drawbacks in this particular scenario, providing an interesting atlas on the convergent application of multiple xAI algorithms in a realistic context. © 2023, The Author(s).

关键词： Explainable Artificial Intelligence High-energy physics Intrinsically interpretable Decision Trees Saliency maps methods Tracing gradient descent

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Unveiling drug-induced side effects through network-based analysis: an update

Unveiling drug-induced side effects through network-based an...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Alessio Funari Federica Conte Paola Paci Department of Translational and Precision Medicine Sapienza University of Rome Institute for Systems Analysis and Computer Science “Antonio Ruberti” National Research Council Rome Italy Institute for Systems Analysis and Computer Science “Antonio Ruberti” National Research Council Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this article, we propose an updated version of our previously developed model for predicting drug-side effect associations, applied to two case studies: long QT syndrome and asthma. The classifier accepts the name of a specific drug side effect as input and outputs a list of drugs potentially associated with this side effect. By simulating how drug effects propagate within the interactome using the Random Walk with Restart algorithm, the classifier identifies genes potentially associated with the onset of the side effect. Based on the rationale that the more a drug perturbs these genes, the more likely it is to cause the side effect, the model identifies drugs potentially linked to the onset of the side effect. Moreover, the model enables the categorization of drugs into chemical subclasses using the ClassyFire schema, facilitating the analysis of complex side effects, such as asthma, through more specific mechanisms. The results show that the model identifies both drugs known to be associated with certain side effects, as well as drugs not officially reported by the FDA, demonstrating its generalizability and practical relevance. This method is also adaptable for analyzing other side effects.

关键词： Drugs Analytical models Adaptation models Predictive models Classification algorithms Bioinformatics Chemicals Asthma

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Digital Twin for mobility: simulation integrated approach for demand matrices calibration with empirical data

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Transportation Research Procedia 2025年 86卷 516-523页

作者： Anna Laura Pala Giovanni Felici Vincenza Torrisi Davide Donato Russo Alessio Salvatore Pierfrancesco Leonardi Giuseppe Stecca Matteo Ignaccolo Giuseppe Inturri National Research Council Institute of Systems Analysis and Computer Science ”Antonio Ruberti” Rome Italy DIAG Sapienza Rome Italy Department of Electric Electronic and Computer Engineering Catania Italy f Department of Bioscience and Territory University of Molise Department of Enterprise Engineering University of Rome ”Tor Vergata” Italy Department of Civil Engineering and Architecture Catania Italy

A Digital Twin for Mobility (DTmob) is based on the digital representation of the real-world transport system. A crucial element underlying the development of such systems is an accurate simulation of traffic flows within the road network, which is generally based on transport demand data in the form of Origin-Destination (O-D) matrices. Estimating demand matrices accurately is a complex challenge, as they are hindered by incomplete data, inaccuracies in transport models and temporal-spatial variability. In this paper we present an integrated methodological approach for the calibration of O-D matrices via the implementation of an iterative calibration procedure incorporating a traffic survey dataset, followed by the assignment procedure performed by a macrosimulation model. This study addresses this issue by employing Nielsen’s Single Path Matrix Estimation Method (SPME) jointly with the macrosimulation tool PTV Visum, developing a script in Python to achieve their integration. We tested it on the real-case study of Catania urban road network (Italy). Several scenarios have been analyzed and evaluated through the GEH statistic as KPI, also providing results with Ordinary Least Square (OLS) method as benchmark. Obtained results prove the validity of our integrated framework as the threshold in terms of the difference between estimated and counted flows was satisfied in all performed analyses. Research findings lay the basis for investigate further calibration algorithms and for exploring the use of real-time data in line with DTmob requirements.

关键词： Digital Twin transport modelling traffic simulation OD matrix calibration

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Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication

arXiv

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arXiv 2024年

作者： Affinita, Daniele Volpi, Flavio Spagnoli, Valerio Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico D. Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy Faculty of Political Science and Sociopsychological Dynamics UNINT University Rome Italy

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots’ coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots’ actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings. © 2024, CC BY.

关键词： Human resource management

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Optimizing accuracy and diversity: a multi-task approach to forecast combinations

arXiv

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arXiv 2023年

作者： Felici, Giovanni Sudoso, antonio M. Institute for Systems Analysis and Computer Science Antonio Ruberti National Research Council Via dei Taurini 19 Rome00185 Italy Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Via Ariosto 25 Rome00185 Italy

Forecast combination involves using multiple forecasts to create a single, more accurate prediction. Recently, feature-based forecasting has been employed to either select the most appropriate forecasting models or to optimize the weights of their combination. In this paper, we present a multi-task optimization paradigm that focuses on solving both problems simultaneously and enriches current operational research approaches to forecasting. In essence, it incorporates an additional learning and optimization task into the standard feature-based forecasting approach, focusing on the identification of an optimal set of forecasting methods. During the training phase, an optimization model with linear constraints and quadratic objective function is employed to identify accurate and diverse methods for each time series. Moreover, within the training phase, a neural network is used to learn the behavior of that optimization model. Once training is completed the candidate set of methods is identified using the network. The proposed approach elicits the essential role of diversity in feature-based forecasting and highlights the interplay between model combination and model selection when optimizing forecasting ensembles. Experimental results on a large set of series from the M4 competition dataset show that our proposal enhances point forecast accuracy compared to state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Beyond the network-based multi-omics data integration in COPD: a pathway-centric analysis

Beyond the network-based multi-omics data integration in COP...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pasquale Sibilio Federica Conte Paola Paci Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Institute for Systems Analysis and Computer Science “Antonio Ruberti” National Research Council Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Chronic obstructive pulmonary disease (COPD) is a lung disease causing hundred thousand of death each year worldwide and defined as a respiratory and airflow impairment majorly due to large and small airways dysfunctions. COPD could be considered as a syndrome that includes disease axes of variable pathological and clinical conditions. Despite recent advances, the comprehension of the molecular mechanisms responsible for COPD disease spectrum is far to be reached. Moreover, we should also consider continuous smoking exposure, which could lead to variability in the disease mechanisms and progression. Network-based multi-omics data integration can help to study the association between molecular determinants on diverse biomolecular layers in a disease context. In a previous study, we leveraged lung RNA-seq and DNA-methylation data of a COPD-control cohort to build a correlation-based integrated network (called coupled network), that helped to unveil genes involved in immune and inflammatory modulation of COPD. Therefore, in our previous study we highlighted the most important genes of the coupled network and inspected their single contribution to disease-related pathways. In this study we aim to overcome this limitation by performing a pathway activity analysis by considering the expression and DNA methylation profiles of coupled network genes. Moreover, we exploit this analysis to study the possible contribution of coupled network genes to the differential disease progression between current and former smokers patients.

关键词： Pathology Lungs Data integration Modulation DNA Chronic obstructive pulmonary disease Biology Bioinformatics Immune system

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