Quaderni MOX
Pubblicazioni
del Laboratorio di Modellistica e Calcolo Scientifico MOX. I lavori riguardano prevalentemente il campo dell'analisi numerica, della statistica e della modellistica matematica applicata a problemi di interesse ingegneristico. Il sito del Laboratorio MOX è raggiungibile
all'indirizzo mox.polimi.it
Trovati 1349 prodotti
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86/2021 - 16/12/2021
Possenti, L.; Cicchetti, A.; Rosati, R.; Cerroni, D.; Costantino, M.L.; Rancati, T.; Zunino, P.
A Mesoscale Computational Model for Microvascular Oxygen Transfer | Abstract | | We address a mathematical model for oxygen transfer in the microcirculation. The model includes blood flow and hematocrit transport coupled with the interstitial flow, oxygen transport in the blood and the tissue, including capillary-tissue exchange effects. Moreover, the model is suited to handle arbitrarily complex vascular geometries. The purpose of this study is the validation of the model with respect to classical solutions and the further demonstration of its adequacy to describe the heterogeneity of oxygenation in the tissue microenvironment. Finally, we discuss the importance of these effects in the treatment of cancer using radiotherapy. |
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85/2021 - 16/12/2021
Cavinato, L., Gozzi, N., Sollini, M., Carlo-Stella, C., Chiti, A., & Ieva, F.
Recurrence-specific supervised graph clustering for subtyping Hodgkin Lymphoma radiomic phenotypes | Abstract | | Abstract— The prediction at baseline of patients at high risk for therapy failure or recurrence would significantly impact on Hodgkin Lymphoma patients treatment, informing clinical practice. Current literature is extensively searching insights in radiomics, a promising framework for high-throughput imaging feature extraction, to derive biomarkers and quantitative prognostic factors from images. However, existing studies are limited by intrinsic radiomic limitations, high dimensionality among others. We propose an exhaustive patient representation and a recurrence-specific multi-view supervised clustering algorithm for estimating patient-to-patient similarity graph and learning recurrence probability. We stratified patients in two risk classes and characterize each group in terms of clinical variables. |
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84/2021 - 16/12/2021
Torti, A.; Galvani, M.; Urbano, V.; Arena, M.; Azzone, G.; Secchi, P.; Vantini, S.
Analysing transportation system reliability: the case study of the metro system of Milan | Abstract | | This paper introduces a methodology to monitor the passenger flow in a subway transport system and analyse the system
reliability under different offer and demand scenarios.
Motivated by a collaboration with ATM - the company responsible for the management of the public transport in Milan - we focus on the subway system of Milan with the aim of helping operation managers to handle the daily access of travellers to the train stations during Covid-19 pandemic. In details, we first apply a calibration procedure to estimate a reliable OD matrices; then, a model able to monitor the passenger flow by estimating, for each train, the number of passengers getting on and off at each station, along with the load factor of the train along the line.
Results highlight the subway sections and the stations most at risk of congestion under different offer and demand scenarios; moreover, eventual queues at each station are estimated.
The proposed approach develops a flexible and scalable method to analyse and monitor any urban railway system in any city. |
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83/2021 - 15/12/2021
Colasuonno, F.; Ferrari F.; Gervasio, P.; Quarteroni, A.
Some evaluations of the fractional p-Laplace operator on radial functions | Abstract | | We face a rigidity problem for the fractional p-Laplace operator to extend to this new framework some tools useful for the linear case. It is known that (-Delta)^s (1-bar xbar ^2)^s_+ and -Delta_p (1-bar x bar^(p/(p-1))) are constant functions in (-1,1) for fixed p and s. We evaluated (-Delta_p)^s(1-bar x bar^(p/(p-1)))^s proving that it is not constant in (-1,1) for some p in (1,+infty) and s in (0,1). This conclusion is obtained numerically thanks to the use of very accurate Gaussian numerical quadrature formulas. |
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82/2021 - 02/12/2021
Massi, M.C.; Ieva, F.
Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification | Abstract | | EEG is a non-invasive powerful system that finds applications in several domains and research areas. Most EEG systems are multi-channel in nature, but multiple channels might include noisy and redundant information and increase computational times of automated EEG decoding algorithms. To reduce the signal-to-noise ratio, improve accuracy and reduce computational time, one may combine channel selection with feature extraction and dimensionality reduction. However, as EEG signals present high inter-subject variability, we introduce a novel algorithm for subject-independent channel selection through representation learning of EEG recordings. The algorithm exploits channel-specific 1D-CNNs as supervised feature extractors to maximize class separability and reduces a high dimensional multi-channel signal into a unique 1-Dimensional representation from which it selects the most relevant channels for classification. The algorithm can be transferred to new signals from new subjects and obtain novel highly informative trial vectors of controlled dimensionality to be fed to any kind of classifier. |
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81/2021 - 02/12/2021
Massi, M.C.; Gasperoni, F.; Ieva, F.; Paganoni, A.
Feature Selection for Imbalanced Data with Deep Sparse Autoencoders Ensemble | Abstract | | Class imbalance is a common issue in many domain applications of learning algorithms. Oftentimes, in the same domains it is much more relevant to correctly classify and profile minority class observations. This need can be addressed by Feature Selection (FS), that offers several further advantages, s.a. decreasing computational costs, aiding inference and interpretability. However, traditional FS techniques may become sub-optimal in the presence of strongly imbalanced data. To achieve FS advantages in this setting, we propose a filtering FS algorithm ranking feature importance on the basis of the Reconstruction Error of a Deep Sparse AutoEncoders Ensemble (DSAEE). We use each DSAE trained only on majority class to reconstruct both classes. From the analysis of the aggregated Reconstruction Error, we determine the
features where the minority class presents a different distribution of values w.r.t. the overrepresented one, thus identifying the most relevant features to discriminate between the two. We empirically demonstrate the efficacy of our algorithm in several experiments, both simulated and on high-dimensional datasets of varying sample size, showcasing its capability to select relevant and generalizable features to profile and classify minority class, outperforming other benchmark FS methods. We also briefly present a real application in radiogenomics, where the methodology was applied successfully. |
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80/2021 - 02/12/2021
Sollini, M., Bartoli, F., Cavinato, L., Ieva, F., Ragni, A., Marciano, A., Zanca, R., Galli, L., Paiar, F., Pasqualetti , F. and Erba P. A.
[18F]FMCH PET/CT biomarkers and similarity analysis to refine the definition of oligometastatic prostate cancer | Abstract | | Background:The role of image-derived biomarkers in recurrent oligometastatic Prostate Cancer (PCa) is unexplored. This paper aimed to evaluate [18F]FMCH PET/CT radiomic analysis in patients with recurrent PCa after primary radical therapy. Specifically, we tested intra-patient lesions similarity in oligometastatic and plurimetastatic PCa, comparing the two most used definitions of oligometastatic disease.Methods:PCa patients eligible for [18F]FMCH PET/CT presenting biochemical failure after first-line curative treat-ments were invited to participate in this prospective observational trial. PET/CT images of 92 patients were visually and quantitatively analyzed. Each patient was classified as oligometastatic or plurimetastatic according to the total number of detected lesions (up to 3 and up to 5 or > 3 and > 5, respectively). Univariate and intra-patient lesions’ similarity analysis were performed.Results: [18F]FMCH PET/CT identified 370 lesions, anatomically classified as regional lymph nodes and distant metastases. Thirty-eight and 54 patients were designed oligometastatic and plurimetastatic, respectively, using a 3-lesion threshold. The number of oligometastic scaled up to 60 patients (thus 32 plurimetastatic patients) with a 5-lesion threshold. Similarity analysis showed high lesions’ heterogeneity. Grouping patients according to the number of metastases, patients with oligometastatic PCa defined with a 5-lesion threshold presented lesions heterogene-ity comparable to plurimetastic patients. Lesions within patients having a limited tumor burden as defined by three lesions were characterized by less heterogeneity.Conclusions:We found a comparable heterogeneity between patients with up to five lesions and plurimetastic patients, while patients with up to three lesions were less heterogeneous than plurimetastatic patients, featuring dif-ferent cells phenotypes in the two groups. Our results supported the use of a 3-lesion threshold to define oligometa-static PCa. |
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79/2021 - 02/12/2021
Ferraccioli, F.; Sangalli, L.M.; Finos, L.
Some first inferential tools for spatial regression with differential regularization | Abstract | | Spatial regression with differential regularization is an innovative approach at the crossroad between functional data analysis and spatial data analysis. These models have been shown to be numerically efficient and capable to handle complex applied problems. On the other hand, their theoretical properties are still largely unexplored. Here we consider the discrete estimators in spatial regression models with differential regularization, obtained after numerical discretization, using an expansion on a finite element basis. We study the consistency and the asymptotic normality of these discrete estimators. We also propose a nonparametric test procedure for the linear part of the models, based on random sign-flipping of the score components. The test exploits an appropriate decomposition of the smoothing matrix, in order to reduce the effect of the spatial dependence, without any parametric assumption on the form of the correlation structure. The proposed test is shown to be superior to parametric alternatives. |
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