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Morand J, Yip S, Velegrakis Y, Lattanzi G, Potestio R, Tubiana L. Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context. Sci Rep 2024; 14:4636. [PMID: 38409411 PMCID: PMC10897296 DOI: 10.1038/s41598-024-54878-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 02/17/2024] [Indexed: 02/28/2024] Open
Abstract
We discuss how to assess the reliability of partial, anonymized mobility data and compare two different methods to identify spatial communities based on movements: Greedy Modularity Clustering (GMC) and the novel Critical Variable Selection (CVS). These capture different aspects of mobility: direct population fluxes (GMC) and the probability for individuals to move between two nodes (CVS). As a test case, we consider movements of Italians before and during the SARS-Cov2 pandemic, using Facebook users' data and publicly available information from the Italian National Institute of Statistics (Istat) to construct daily mobility networks at the interprovincial level. Using the Perron-Frobenius (PF) theorem, we show how the mean stochastic network has a stationary population density state comparable with data from Istat, and how this ceases to be the case if even a moderate amount of pruning is applied to the network. We then identify the first two national lockdowns through temporal clustering of the mobility networks, define two representative graphs for the lockdown and non-lockdown conditions and perform optimal spatial community identification on both graphs using the GMC and CVS approaches. Despite the fundamental differences in the methods, the variation of information (VI) between them assesses that they return similar partitions of the Italian provincial networks in both situations. The information provided can be used to inform policy, for example, to define an optimal scale for lockdown measures. Our approach is general and can be applied to other countries or geographical scales.
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Affiliation(s)
- Jules Morand
- University of Trento, via Sommarive 14, 38123, Trento, Italy.
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, 38123, Trento, Italy.
| | - Shoichi Yip
- University of Trento, via Sommarive 14, 38123, Trento, Italy
| | - Yannis Velegrakis
- University of Trento, via Sommarive 14, 38123, Trento, Italy
- Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands
| | - Gianluca Lattanzi
- University of Trento, via Sommarive 14, 38123, Trento, Italy
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, 38123, Trento, Italy
| | - Raffaello Potestio
- University of Trento, via Sommarive 14, 38123, Trento, Italy
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, 38123, Trento, Italy
| | - Luca Tubiana
- University of Trento, via Sommarive 14, 38123, Trento, Italy
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, 38123, Trento, Italy
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Lakhal S, Darmon A, Mastromatteo I, Marsili M, Benzaquen M. Multiscale relevance of natural images. Sci Rep 2023; 13:14879. [PMID: 37689770 PMCID: PMC10492821 DOI: 10.1038/s41598-023-41714-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/30/2023] [Indexed: 09/11/2023] Open
Abstract
We use an agnostic information-theoretic approach to investigate the statistical properties of natural images. We introduce the Multiscale Relevance (MSR) measure to assess the robustness of images to compression at all scales. Starting in a controlled environment, we characterize the MSR of synthetic random textures as function of image roughness [Formula: see text] and other relevant parameters. We then extend the analysis to natural images and find striking similarities with critical ([Formula: see text]) random textures. We show that the MSR is more robust and informative of image content than classical methods such as power spectrum analysis. Finally, we confront the MSR to classical measures for the calibration of common procedures such as color mapping and denoising. Overall, the MSR approach appears to be a good candidate for advanced image analysis and image processing, while providing a good level of physical interpretability.
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Affiliation(s)
- Samy Lakhal
- Chair of Econophysics and Complex Systems, Ecole Polytechnique, 91128, Palaiseau Cedex, France
- LadHyX, UMR CNRS 7646, Ecole Polytechnique, 91128, Palaiseau Cedex, France
- Institut Jean Le Rond d'Alembert, UMR CNRS 7190, Sorbonne Université, 75005, Paris, France
| | | | - Iacopo Mastromatteo
- Chair of Econophysics and Complex Systems, Ecole Polytechnique, 91128, Palaiseau Cedex, France
- Capital Fund Management, 23 Rue de l'Université, 75007, Paris, France
| | - Matteo Marsili
- Quantitative Life Sciences Section, The Abdus Salam International Centre for Theoretical Physics, 34151, Trieste, Italy
| | - Michael Benzaquen
- Chair of Econophysics and Complex Systems, Ecole Polytechnique, 91128, Palaiseau Cedex, France.
- LadHyX, UMR CNRS 7646, Ecole Polytechnique, 91128, Palaiseau Cedex, France.
- Capital Fund Management, 23 Rue de l'Université, 75007, Paris, France.
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Holtzman R, Giulini M, Potestio R. Making sense of complex systems through resolution, relevance, and mapping entropy. Phys Rev E 2022; 106:044101. [PMID: 36397524 DOI: 10.1103/physreve.106.044101] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 08/16/2022] [Indexed: 06/16/2023]
Abstract
Complex systems are characterized by a tight, nontrivial interplay of their constituents, which gives rise to a multiscale spectrum of emergent properties. In this scenario, it is practically and conceptually difficult to identify those degrees of freedom that mostly determine the behavior of the system and separate them from less prominent players. Here, we tackle this problem making use of three measures of statistical information: Resolution, relevance, and mapping entropy. We address the links existing among them, taking the moves from the established relation between resolution and relevance and further developing novel connections between resolution and mapping entropy; by these means we can identify, in a quantitative manner, the number and selection of degrees of freedom of the system that preserve the largest information content about the generative process that underlies an empirical dataset. The method, which is implemented in a freely available software, is fully general, as it is shown through the application to three very diverse systems, namely, a toy model of independent binary spins, a coarse-grained representation of the financial stock market, and a fully atomistic simulation of a protein.
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Affiliation(s)
- Roi Holtzman
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Marco Giulini
- Physics Department, University of Trento, via Sommarive, 14 I-38123 Trento, Italy
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, I-38123 Trento, Italy
| | - Raffaello Potestio
- Physics Department, University of Trento, via Sommarive, 14 I-38123 Trento, Italy
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, I-38123 Trento, Italy
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Mele M, Covino R, Potestio R. Information-theoretical measures identify accurate low-resolution representations of protein configurational space. SOFT MATTER 2022; 18:7064-7074. [PMID: 36070256 DOI: 10.1039/d2sm00636g] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The steadily growing computational power employed to perform molecular dynamics simulations of biological macromolecules represents at the same time an immense opportunity and a formidable challenge. In fact, large amounts of data are produced, from which useful, synthetic, and intelligible information has to be extracted to make the crucial step from knowing to understanding. Here we tackled the problem of coarsening the conformational space sampled by proteins in the course of molecular dynamics simulations. We applied different schemes to cluster the frames of a dataset of protein simulations; we then employed an information-theoretical framework, based on the notion of resolution and relevance, to gauge how well the various clustering methods accomplish this simplification of the configurational space. Our approach allowed us to identify the level of resolution that optimally balances simplicity and informativeness; furthermore, we found that the most physically accurate clustering procedures are those that induce an ultrametric structure of the low-resolution space, consistently with the hypothesis that the protein conformational landscape has a self-similar organisation. The proposed strategy is general and its applicability extends beyond that of computational biophysics, making it a valuable tool to extract useful information from large datasets.
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Affiliation(s)
- Margherita Mele
- Physics Department, University of Trento, via Sommarive, 14 I-38123 Trento, Italy.
| | - Roberto Covino
- Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
| | - Raffaello Potestio
- Physics Department, University of Trento, via Sommarive, 14 I-38123 Trento, Italy.
- INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, I-38123 Trento, Italy
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Luo G, Yang Z, Zhang Q. Identification of autonomous nonlinear dynamical system based on discrete-time multiscale wavelet neural network. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06142-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble. ENTROPY 2020; 22:e22080880. [PMID: 33286650 PMCID: PMC7517484 DOI: 10.3390/e22080880] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 08/04/2020] [Accepted: 08/06/2020] [Indexed: 11/17/2022]
Abstract
The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is associated with different features of the stimulus. By calculating a neural ensemble’s information in response to a mixed stimulus comprising slow and fast signals, we show that the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code.
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Capizzi G, Lo Sciuto G, Napoli C, Woźniak M, Susi G. A spiking neural network-based long-term prediction system for biogas production. Neural Netw 2020; 129:271-279. [PMID: 32569855 DOI: 10.1016/j.neunet.2020.06.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 05/29/2020] [Accepted: 06/01/2020] [Indexed: 11/25/2022]
Abstract
Efficient energy production from biomass is a central issue in the context of clean alternative energy resource. In this work we propose a novel model based on spiking neural networks cubes in order to model the chemical processes that goes on in a digestor for the production of usable biogas. For the implementation of the predictive structure, we have used the NeuCube computational framework. The goals of the proposed model were: develop a tool for real applications (low-cost and efficient), generalize the data when the system presents high sensitivity to small differences on the initial conditions, take in account the "multi-scale" temporal dynamics of the chemical processes occurring in the digestor, since the variations present in the early stages of the processes are very quick, whereas in the later stages are slower. By using the first ten days of observation the implemented system has been proven able to predict the evolution of the chemical process up to the 100th day obtaining a high degree of accuracy with respect to the experimental data measured in laboratory. This is due to the fact that the spiking neural networks have shown to be able to modeling complex information processes and then it has been shown that spiking neurons are able to handle patterns of activity that spans different time scales. Thanks to such properties, our system is able to capture the multi-scale trend of the time series associated to the early-stage evolutions, as well as their interaction, which are crucial in the point of view of the information content to obtain a good long-term prediction.
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Affiliation(s)
- Giacomo Capizzi
- Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy; Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44100 Gliwice, Poland
| | - Grazia Lo Sciuto
- Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
| | - Christian Napoli
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Roma, Italy.
| | - Marcin Woźniak
- Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44100 Gliwice, Poland
| | - Gianluca Susi
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Technical University of Madrid Madrid, Spain; Department of Experimental Psychology, Cognitive Processes and Logopedy, Complutense University of Madrid, Spain
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