MOX Reports
The preprint collection of the Laboratory for Modeling and Scientific Computation MOX. It mainly contains works on numerical
analysis and mathematical modeling applied to engineering problems. MOX web site is mox.polimi.it
Found 1287 products
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69/2020 - 11/07/2020
Galvani, M.; Torti, A.; Menafoglio, A.; Vantini S.
FunCC: a new bi-clustering algorithm for functional data with misalignment | Abstract | | The problem of bi-clustering functional data, which has recently been addressed in literature, is considered. A definition of ideal functional bi-cluster is given and a novel bi-clustering method, called Functional Cheng and Church (FunCC), is developed. The introduced algorithm searches for non-overlapping and non-exhaustive bi-clusters in a set of functions which are naturally ordered in matrix structure through a non-parametric deterministic iterative procedure.
Moreover, the possible misalignment of the data, which is a common problem when dealing with functions, is taken into account. Hence, the FunCC algorithm is extended obtaining a model able to jointly bi-cluster and align curves.
Different simulation studies are performed to show the potential of the introduced method and to compare it with state-of-the-art methods. The model is also applied on a real case study allowing to discover the spatio-temporal patterns of a bike-sharing system infrastructure.
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67/2020 - 11/07/2020
Caramenti, L.; Menafoglio, A.; Sgobba, S.; Lanzano, G.
Multi-Source Geographically Weighted Regression for Regionalized Ground-Motion Models | Abstract | | This work proposes a novel approach to the calibration of regionalized regression models, with particular reference to ground-motion models (GMMs), which are key for probabilistic seismic hazard analysis and earthquake engineering applications. A novel methodology, named multi-source geographically-weighted regression (MS-GWR), is developed, allowing one to (i) estimate regionalized regression models depending on multiple sources of non-stationarity (such as site- and event-dependent non-stationarities in GMMs), and (ii) make inference on the significance and stationarity of the regression coefficients. Unlike previous approaches to the problem, the proposed framework is fully non-parametric, the inference being based on a permutation scheme. MS-GWR is here used to calibrate a new regionalized ground-motion model for predicting peak ground acceleration in Italy, based on a large scale database of waveforms and metadata made available by the Italian Institute for Geophysics and Vulcanology (INGV). |
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66/2020 - 10/29/2020
Didkovsky, O.; Ivanov, V.; Papini, M.; Longoni, L.; Menafoglio, A.
A comparison between machine learning and functional geostatistics approaches for data-driven analyses of solid transport in a pre-Alpine stream | Abstract | | We address the problem of providing data-driven models for sediment transport in a pre-Alpine stream in Italy. We base our study on a large set of measurements collected from real pebbles, traced along the stream through Radio Frequency IDentificator (RFID) tags after precipitation events. We propose and evaluate two classes of data-driven models -- based on machine learning and functional geostatistics approaches respectively -- to predict the probability of movement of single pebbles within the stream. The first class is built upon gradient boosting decision trees, and allows one to estimate the probability of movement of a pebble, based on the pebbles’ geometrical features, river flow rate, locations, and subdomain types. The second class is built upon functional kriging, a recent geostatistical technique which allows one to predict a functional profile --i.e., the movement probability of a pebble, as a function of the pebbles geometrical features or of the stream's flow rate-- at unsampled locations in the study area.
Although grounded on different perspectives, both these models aim to account for two main sources of uncertainty, namely (i) the complexity of river’s morphological structure, and (ii) the highly-nonlinear dependence between probability of movement, pebble’s size and shape, and the stream’s flow rate.
We extensively compare the performances of the two methods in terms of classification accuracy, and show that, although these techniques are grounded on different perspectives, an overall consistency appears between the methods suggesting that both approaches may provide valuable modeling frameworks for the problem at hand. We finally discuss on the use of the developed models in a bottom-up strategy, which starts with the prediction/classification of a single pebble and then integrates the results into a forecast of the grain-size distribution of mobilized sediments. |
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64/2020 - 10/16/2020
Fiz, F.; Viganò, L.; Gennaro, N.; Costa, G.; La Bella, L.; Boichuk A.; Cavinato, L.; Sollini, M.; Politi, L. S.; Chiti, A.; Torzilli, G.
Radiomics of Liver Metastases: A Systematic Review | Abstract | | Patients with liver metastases can be scheduled for different therapies
(e.g., chemotherapy, surgery, radiotherapy, and ablation). The choice of the most appropriate treatment should rely on adequate understanding of tumor biology and prediction of survival, but reliable biomarkers are lacking. Radiomics is an innovative approach to medical imaging: it identifies invisible-to-the-human-eye radiological patterns that can predict tumor aggressiveness and patients outcome. We reviewed the available literature to elucidate the role of radiomics in patients with liver metastases. Thirty-two papers were analyzed, mostly (56%) concerning metastases from colorectal cancer. Even if available studies are still preliminary, radiomics provided effective prediction of response to chemotherapy and of survival, allowing more accurate and earlier prediction than standard predictors. Entropy and homogeneity were the radiomic features with the strongest clinical impact. In the next few years, radiomics is expected to give a consistent contribution to the precision medicine approach to patients with liver metastases. |
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63/2020 - 09/30/2020
Tuveri, M.; Milani, E.; Marchegiani, G.; Landoni, L.; Torresani, E.; Capelli, P.; Sperandio, N.; D’Onofrio, M.; Salvia, R.; Vergara, C.; Bassi, C.
HEMODYNAMICS AND REMODELING OF THE PORTAL CONFLUENCE IN PATIENTS WITH CANCER OF THE PANCREATIC HEAD: A PILOT STUDY | Abstract | | We designed a computational study to evaluate the effects of hemodynamics on portal confluence remodeling in cancer of the pancreatic head. The importance of this study is the finding that altered flow conditions due to tumor growth can disrupt the balance between eutrophic remodeling and degradative process of the vein wall, leading to the complete substitution of the three-layered vein wall and the opportunity to perform a total pancreatectomy with en-bloc resection of the portal confluence. |
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62/2020 - 09/14/2020
Massi, M. C.; Ieva, F.
Representation Learning Methods 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. At the moment, most EEG systems require subjects to wear several electrodes on the scalp. However, a large number of channels might include noisy information, redundant signals, induce longer preparation times and increase the computational times of any automated system trying to classify EEG recordings. One way to reduce the signal-to-noise ratio and improve the classification accuracy is to combine channel selection with feature extraction. However, when dealing with EEG channel selection most of the efforts have been focused on identifying the most relevant channels in a subject-dependent fashion. In this paper we introduce a novel algorithm for subject independent channel selection of EEG recordings.
In particular, the algorithm (i) exploits channel-specific Representation Learning Methods to compress signals from various channels, (ii) builds a unique representation of each trial by concatenating the channels' representations into a unique low-dimensional vector and (iii) selects from these vectors the most relevant channels to perform classification. After training, the algorithm can be exploited by (iv) transferring the parametrized subgroup of selected channel-specific RLMs to new signals and (v) obtain novel trial vectors to be fed to any kind of classifier. We tested the algorithm on a case study attaining extremely promising results when considering the complexity of subject independent channel selection. |
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61/2020 - 09/10/2020
Pozzi, S.; Redaelli, A.; Vergara, C.; Votta, E.; Zunino, P.
Mathematical and numerical modeling of atherosclerotic plaque progression based on fluid-structure interaction | Abstract | | In this work we propose a mathematical and numerical model to describe the early stages of atherosclerotic plaque formation,
which is based on the interaction of processes with different spatial and temporal scales.
A fluid-structure interaction problem, used to describe the cardiovascular mechanics arising between blood and the artery wall, is coupled to a set of differential problems describing the evolution of solute concentrations.
In order to manage the multiscale-in-space nature of the involved processes, we propose a suitable numerical strategy based on the splitting and sequential solution of the coupled problem.
We present some preliminary numerical results and investigate the effects of geometry, model parameters and coupling strategy on plaque growth. |
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60/2020 - 08/19/2020
Lupo Pasini, M; Perotto, S.
Hierarchical model reduction driven by a Proper Orthogonal Decomposition for parametrized advection-diffusion-reaction problems | Abstract | | This work combines the Hierarchical Model (HiMod) reduction technique with a standard Proper Orthogonal Decomposition (POD) to solve parametrized partial differential equations modeling advection-diffusion-reaction phenomena in elongated domains (e.g., pipes). This combination leads to what we define a HiPOD model reduction, which merges the reliability of HiMod with the computational efficiency of POD. Two different HiPOD techniques are presented and assessed through an extensive numerical verification. |
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