Publications List
2023
Arfat, Yasir; Mittone, Gianluca; Colonnelli, Iacopo; D'Ascenzo, Fabrizio; Esposito, Roberto; Aldinucci, Marco
Pooling critical datasets with Federated Learning Proceedings Article
In: 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023, pp. 329–337, IEEE, Napoli, Italy, 2023.
@inproceedings{23:praise-fl:pdp,
title = {Pooling critical datasets with Federated Learning},
author = {Yasir Arfat and Gianluca Mittone and Iacopo Colonnelli and Fabrizio D'Ascenzo and Roberto Esposito and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/491e22ec-3db5-4989-a063-085a199edd20/23_pdp_fl.pdf},
doi = {10.1109/PDP59025.2023.00057},
year = {2023},
date = {2023-01-01},
booktitle = {31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023},
pages = {329–337},
publisher = {IEEE},
address = {Napoli, Italy},
abstract = {Federated Learning (FL) is becoming popular in different industrial sectors where data access is critical for security, privacy and the economic value of data itself. Unlike traditional machine learning, where all the data must be globally gathered for analysis, FL makes it possible to extract knowledge from data distributed across different organizations that can be coupled with different Machine Learning paradigms. In this work, we replicate, using Federated Learning, the analysis of a pooled dataset (with AdaBoost) that has been used to define the PRAISE score, which is today among the most accurate scores to evaluate the risk of a second acute myocardial infarction. We show that thanks to the extended-OpenFL framework, which implements AdaBoost.F, we can train a federated PRAISE model that exhibits comparable accuracy and recall as the centralised model. We achieved F1 and F2 scores which are consistently comparable to the PRAISE score study of a 16- parties federation but within an order of magnitude less time.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
Polato, Mirko; Esposito, Roberto; Aldinucci, Marco
Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent Proceedings Article
In: Intl. Joint Conference on Neural Networks (IJCNN), pp. 1–10, IEEE, Padua, Italy, 2022.
@inproceedings{22:fl:ijcnn,
title = {Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent},
author = {Mirko Polato and Roberto Esposito and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/03a7b692-aecc-43db-a792-874c553d9ebe/ijcnn22-internal.pdf},
doi = {10.1109/IJCNN55064.2022.9892284},
year = {2022},
date = {2022-07-01},
booktitle = {Intl. Joint Conference on Neural Networks (IJCNN)},
pages = {1–10},
publisher = {IEEE},
address = {Padua, Italy},
abstract = {Federated Learning has been proposed to develop better AI systems without compromising the privacy of final users and the legitimate interests of private companies. Initially deployed by Google to predict text input on mobile devices, FL has been deployed in many other industries. Since its introduction, Federated Learning mainly exploited the inner working of neural networks and other gradient descent-based algorithms by either exchanging the weights of the model or the gradients computed during learning. While this approach has been very successful, it rules out applying FL in contexts where other models are preferred, e.g., easier to interpret or known to work better. This paper proposes FL algorithms that build federated models without relying on gradient descent-based methods. Specifically, we leverage distributed versions of the AdaBoost algorithm to acquire strong federated models. In contrast with previous approaches, our proposal does not put any constraint on the client-side learning models. We perform a large set of experiments on ten UCI datasets, comparing the algorithms in six non-iidness settings.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Tartaglione, Enzo; Zapater, Marina
In: Technologies and Applications for Big Data Value, pp. 183, 2022.
@article{tartaglione2022marco,
title = {Marco Aldinucci, David Atienza, Federico Bolelli, Mónica Caballero, Iacopo Colonnelli, José Flich, Jon A. Gómez, David González, Costantino Grana, Marco Grangetto, Simone Leo, Pedro López, Dana Oniga, Roberto Paredes, Luca Pireddu, Eduardo Qui~nones, Tatiana Silva},
author = {Enzo Tartaglione and Marina Zapater},
url = {https://iris.unimore.it/retrieve/handle/11380/1230906/420166/2021bdva.pdf},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Technologies and Applications for Big Data Value},
pages = {183},
publisher = {Springer Nature},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sulis, Emilio; Amantea, Ilaria Angela; Aldinucci, Marco; Boella, Guido; Marinello, Renata; Grosso, Marco; Platter, Paolo; Ambrosini, Serena
An ambient assisted living architecture for hospital at home coupled with a process-oriented perspective Journal Article
In: Journal of Ambient Intelligence and Humanized Computing, pp. 1–19, 2022.
@article{sulis2022ambient,
title = {An ambient assisted living architecture for hospital at home coupled with a process-oriented perspective},
author = {Emilio Sulis and Ilaria Angela Amantea and Marco Aldinucci and Guido Boella and Renata Marinello and Marco Grosso and Paolo Platter and Serena Ambrosini},
url = {https://link.springer.com/article/10.1007/s12652-022-04388-6},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Journal of Ambient Intelligence and Humanized Computing},
pages = {1--19},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aldinucci, Marco; Atienza, David; Bolelli, Federico; Caballero, Mónica; Colonnelli, Iacopo; Flich, José; Gómez, Jon A; González, David; Grana, Costantino; Grangetto, Marco; others,
In: Technologies and Applications for Big Data Value, pp. 183–202, Springer, 2022.
@incollection{aldinucci2022deephealth,
title = {The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures},
author = {Marco Aldinucci and David Atienza and Federico Bolelli and Mónica Caballero and Iacopo Colonnelli and José Flich and Jon A Gómez and David González and Costantino Grana and Marco Grangetto and others},
url = {https://link.springer.com/chapter/10.1007/978-3-030-78307-5_9},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Technologies and Applications for Big Data Value},
pages = {183--202},
publisher = {Springer},
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pubstate = {published},
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Colonnelli, Iacopo; Aldinucci, Marco; Cantalupo, Barbara; Padovani, Luca; Rabellino, Sergio; Spampinato, Concetto; Morelli, Roberto; Carlo, Rosario Di; Magini, Nicol`o; Cavazzoni, Carlo
Distributed workflows with Jupyter Journal Article
In: Future Generation Computer Systems, vol. 128, pp. 282–298, 2022.
@article{colonnelli2022distributed,
title = {Distributed workflows with Jupyter},
author = {Iacopo Colonnelli and Marco Aldinucci and Barbara Cantalupo and Luca Padovani and Sergio Rabellino and Concetto Spampinato and Roberto Morelli and Rosario Di Carlo and Nicol`o Magini and Carlo Cavazzoni},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X21003976},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Future Generation Computer Systems},
volume = {128},
pages = {282--298},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
nones, Eduardo Qui; Perales, Jesus; Ejarque, Jorge; Badouh, Asaf; Marco, Santiago; Auzanneau, Fabrice; Galea, François; González, David; Hervás, José Ramón; Silva, Tatiana; others,
In: HPC, Big Data, and AI Convergence Towards Exascale, pp. 191–216, CRC Press, 2022.
@incollection{quinones2022deephealth,
title = {The DeepHealth HPC Infrastructure: Leveraging Heterogenous HPC and Cloud-Computing Infrastructures for IA-Based Medical Solutions},
author = {Eduardo Qui nones and Jesus Perales and Jorge Ejarque and Asaf Badouh and Santiago Marco and Fabrice Auzanneau and François Galea and David González and José Ramón Hervás and Tatiana Silva and others},
url = {https://iris.unito.it/bitstream/2318/1832050/3/Preprint.pdf},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {HPC, Big Data, and AI Convergence Towards Exascale},
pages = {191--216},
publisher = {CRC Press},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Perlo, Daniele; Tartaglione, Enzo; Gava, Umberto; D’Agata, Federico; Benninck, Edwin; Bergui, Mauro
UniToBrain Dataset: A Brain Perfusion Dataset Proceedings Article
In: International Conference on Image Analysis and Processing, pp. 498–509, Springer 2022.
@inproceedings{perlo2022unitobrain,
title = {UniToBrain Dataset: A Brain Perfusion Dataset},
author = {Daniele Perlo and Enzo Tartaglione and Umberto Gava and Federico D’Agata and Edwin Benninck and Mauro Bergui},
url = {https://link.springer.com/chapter/10.1007/978-3-031-13321-3_44},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {International Conference on Image Analysis and Processing},
pages = {498--509},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Polato, Mirko; Esposito, Roberto; Aldinucci, Marco
Boosting the federation: Cross-silo federated learning without gradient descent Proceedings Article
In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–10, IEEE 2022.
@inproceedings{polato2022boosting,
title = {Boosting the federation: Cross-silo federated learning without gradient descent},
author = {Mirko Polato and Roberto Esposito and Marco Aldinucci},
url = {https://ieeexplore.ieee.org/abstract/document/9892284},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},
pages = {1--10},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Baccega, Daniele; Pernice, Simone; Terna, Pietro; Castagno, Paolo; Moirano, Giovenale; Richiardi, Lorenzo; Sereno, Matteo; Rabellino, Sergio; Maule, Milena; Beccuti, Marco; others,
An Agent-Based Model to Support Infection Control Strategies at School Journal Article
In: JASSS, vol. 25, no. 3, pp. 1–15, 2022.
@article{baccega2022agent,
title = {An Agent-Based Model to Support Infection Control Strategies at School},
author = {Daniele Baccega and Simone Pernice and Pietro Terna and Paolo Castagno and Giovenale Moirano and Lorenzo Richiardi and Matteo Sereno and Sergio Rabellino and Milena Maule and Marco Beccuti and others},
url = {https://iris.unito.it/bitstream/2318/1875158/1/Baccega2022.pdf},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {JASSS},
volume = {25},
number = {3},
pages = {1--15},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Iacopo, Colonnelli; others,
Workflow models for heterogeneous distributed systems Journal Article
In: 2022.
@article{iacopo2022workflow,
title = {Workflow models for heterogeneous distributed systems},
author = {Colonnelli Iacopo and others},
url = {https://iris.unito.it/handle/2318/1875538},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Paseri, Ludovica
The European Approach to Open Science and Research Data PhD Thesis
University of Luxembourg, Luxembourg, 2022.
@phdthesis{paseri2022european,
title = {The European Approach to Open Science and Research Data},
author = {Ludovica Paseri},
year = {2022},
date = {2022-01-01},
school = {University of Luxembourg, Luxembourg},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Paseri, Ludovica
The European legal approach to Open Science and research data Journal Article
In: 2022.
@article{paseri2022europeanb,
title = {The European legal approach to Open Science and research data},
author = {Ludovica Paseri},
url = {http://amsdottorato.unibo.it/10393/},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {alma},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Salanitri, Federica Proietto; Bellitto, Giovanni; Palazzo, Simone; Irmakci, Ismail; Wallace, Michael B.; Bolan, Candice W.; Engels, Megan; Hoogenboom, Sanne; Aldinucci, Marco; Bagci, Ulas; Giordano, Daniela; Spampinato, Concetto
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images Proceedings Article
In: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022, pp. 475–479, IEEE, 2022.
@inproceedings{DBLP:conf/embc/SalanitriBPIWBE22,
title = {Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images},
author = {Federica Proietto Salanitri and Giovanni Bellitto and Simone Palazzo and Ismail Irmakci and Michael B. Wallace and Candice W. Bolan and Megan Engels and Sanne Hoogenboom and Marco Aldinucci and Ulas Bagci and Daniela Giordano and Concetto Spampinato},
url = {https://doi.org/10.1109/EMBC48229.2022.9871547},
doi = {10.1109/EMBC48229.2022.9871547},
year = {2022},
date = {2022-01-01},
booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022},
pages = {475–479},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2021
Colonnelli, Iacopo; Cantalupo, Barbara; Esposito, Roberto; Pennisi, Matteo; Spampinato, Concetto; Aldinucci, Marco
HPC application cloudification: The StreamFlow toolkit Proceedings Article
In: 12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021), Schloss Dagstuhl-Leibniz-Zentrum für Informatik 2021.
@inproceedings{colonnelli2021hpc,
title = {HPC application cloudification: The StreamFlow toolkit},
author = {Iacopo Colonnelli and Barbara Cantalupo and Roberto Esposito and Matteo Pennisi and Concetto Spampinato and Marco Aldinucci},
url = {https://drops.dagstuhl.de/opus/volltexte/2021/13641/},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021)},
organization = {Schloss Dagstuhl-Leibniz-Zentrum für Informatik},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Marco, Aldinucci; David, Atienza; Bolelli, Federico; Mónica, Caballero; Iacopo, Colonnelli; José, Flich; Gómez, Jon A; David, González; Grana, Costantino; Marco, Grangetto; others,
In: 2021.
@article{marco2021deephealth,
title = {The DeepHealth toolkit: a key European free and open-source software for Deep Learning and Computer Vision ready to exploit heterogeneous HPC and cloud architectures},
author = {Aldinucci Marco and Atienza David and Federico Bolelli and Caballero Mónica and Colonnelli Iacopo and Flich José and Jon A Gómez and González David and Costantino Grana and Grangetto Marco and others},
url = {https://iris.unimore.it/handle/11380/1230906},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Beccuti, Marco; Bonnici, Vincenzo; Giugno, Rosalba
MODIMO: Workshop on Multi-Omics Data Integration for Modelling Biological Systems Proceedings Article
In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 4870–4871, 2021.
@inproceedings{beccuti2021modimo,
title = {MODIMO: Workshop on Multi-Omics Data Integration for Modelling Biological Systems},
author = {Marco Beccuti and Vincenzo Bonnici and Rosalba Giugno},
url = {https://dl.acm.org/doi/abs/10.1145/3459637.3482038},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Proceedings of the 30th ACM International Conference on Information & Knowledge Management},
pages = {4870--4871},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bontempi, Gianluca; Chavarriaga, Ricardo; Canck, Hans; Girardi, Emanuela; Hoos, Holger; Kilbane-Dawe, Iarla; Ball, Tonio; Nowé, Ann; Sousa, Jose; Bacciu, Davide; others,
The CLAIRE COVID-19 initiative: approach, experiences and recommendations Journal Article
In: Ethics and information technology, vol. 23, no. 1, pp. 127–133, 2021.
@article{bontempi2021claire,
title = {The CLAIRE COVID-19 initiative: approach, experiences and recommendations},
author = {Gianluca Bontempi and Ricardo Chavarriaga and Hans Canck and Emanuela Girardi and Holger Hoos and Iarla Kilbane-Dawe and Tonio Ball and Ann Nowé and Jose Sousa and Davide Bacciu and others},
url = {https://link.springer.com/article/10.1007/s10676-020-09567-7},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Ethics and information technology},
volume = {23},
number = {1},
pages = {127--133},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sulis, Emilio; Cordero, Alex; Donetti, Simone; Ferrero, Paolo; Violato, Andrea
A Framework for Project Risk Assessment in Telehealth Proceedings Article
In: 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 216–221, IEEE 2021.
@inproceedings{sulis2021framework,
title = {A Framework for Project Risk Assessment in Telehealth},
author = {Emilio Sulis and Alex Cordero and Simone Donetti and Paolo Ferrero and Andrea Violato},
url = {https://ieeexplore.ieee.org/abstract/document/9697956},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)},
pages = {216--221},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
D’Agostino, Daniele; Li`o, Pietro; Aldinucci, Marco; Merelli, Ivan
Advantages of using graph databases to explore chromatin conformation capture experiments Journal Article
In: BMC bioinformatics, vol. 22, no. 2, pp. 1–16, 2021.
@article{d2021advantages,
title = {Advantages of using graph databases to explore chromatin conformation capture experiments},
author = {Daniele D’Agostino and Pietro Li`o and Marco Aldinucci and Ivan Merelli},
url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03937-0},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {BMC bioinformatics},
volume = {22},
number = {2},
pages = {1--16},
publisher = {BioMed Central},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sulis, Emilio; Terna, Pietro
An agent-based decision support for a vaccination campaign Journal Article
In: Journal of Medical Systems, vol. 45, no. 11, pp. 1–7, 2021.
@article{sulis2021agent,
title = {An agent-based decision support for a vaccination campaign},
author = {Emilio Sulis and Pietro Terna},
url = {https://link.springer.com/article/10.1007/s10916-021-01772-1},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Journal of Medical Systems},
volume = {45},
number = {11},
pages = {1--7},
publisher = {Springer},
keywords = {},
pubstate = {published},
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}
Perlo, Daniele
Deep Learning for medical imaging: perspectives from real use cases Journal Article
In: 2021.
@article{perlo2021deep,
title = {Deep Learning for medical imaging: perspectives from real use cases},
author = {Daniele Perlo},
url = {https://iris.unito.it/bitstream/2318/1822934/1/DanielePerlo___Unito___PhD_thesis.pdf},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
keywords = {},
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}
Aldinucci, Marco; Cesare, Valentina; Colonnelli, Iacopo; Martinelli, Alberto Riccardo; Mittone, Gianluca; Cantalupo, Barbara
Practical Parallelizazion of a Laplace Solver with MPI Proceedings Article
In: Iannone, Francesco (Ed.): ENEA CRESCO in the fight against COVID-19, pp. 21–24, ENEA, 2021.
@inproceedings{21:laplace:enea,
title = {Practical Parallelizazion of a Laplace Solver with MPI},
author = {Marco Aldinucci and Valentina Cesare and Iacopo Colonnelli and Alberto Riccardo Martinelli and Gianluca Mittone and Barbara Cantalupo},
editor = {Francesco Iannone},
year = {2021},
date = {2021-01-01},
booktitle = {ENEA CRESCO in the fight against COVID-19},
pages = {21–24},
publisher = {ENEA},
abstract = {This work exposes a practical methodology for the semi-automatic parallelization of existing code. We show how a scientific sequential code can be parallelized through our approach. The obtained parallel code is only slightly different from the starting sequential one, providing an example of how little re-designing our methodology involves. The performance of the parallelized code, executed on the CRESCO6 cluster, is then exposed and discussed. We also believe in the educational value of this approach and suggest its use as a teaching device for students.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Arfat, Yasir; Mittone, Gianluca; Esposito, Roberto; Cantalupo, Barbara; Ferrari, Gaetano Maria De; Aldinucci, Marco
A Review of Machine Learning for Cardiology Journal Article
In: Minerva cardiology and angiology, 2021.
@article{21:ai4numbers:minerva,
title = {A Review of Machine Learning for Cardiology},
author = {Yasir Arfat and Gianluca Mittone and Roberto Esposito and Barbara Cantalupo and Gaetano Maria De Ferrari and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/handle/2318/1796298/780512/21_AI4numbers-preprint.pdf},
doi = {10.23736/s2724-5683.21.05709-4},
year = {2021},
date = {2021-01-01},
journal = {Minerva cardiology and angiology},
abstract = {This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
D'Agostino, Daniele; Liò, Pietro; Aldinucci, Marco; Merelli, Ivan
Advantages of using graph databases to explore chromatin conformation capture experiments Journal Article
In: BMC Bioinformatics, vol. 22, no. 2, pp. 43–58, 2021, ISBN: 1471-2105.
@article{21:neohic:bmc,
title = {Advantages of using graph databases to explore chromatin conformation capture experiments},
author = {Daniele D'Agostino and Pietro Liò and Marco Aldinucci and Ivan Merelli},
url = {https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-020-03937-0.pdf},
doi = {10.1186/s12859-020-03937-0},
isbn = {1471-2105},
year = {2021},
date = {2021-01-01},
journal = {BMC Bioinformatics},
volume = {22},
number = {2},
pages = {43–58},
abstract = {High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Colonnelli, Iacopo; Cantalupo, Barbara; Esposito, Roberto; Pennisi, Matteo; Spampinato, Concetto; Aldinucci, Marco
HPC Application Cloudification: The StreamFlow Toolkit Proceedings Article
In: Bispo, João; Cherubin, Stefano; Flich, José (Ed.): 12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021), pp. 5:1–5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl, Germany, 2021, ISSN: 2190-6807.
@inproceedings{colonnelli_et_al:OASIcs.PARMA-DITAM.2021.5,
title = {HPC Application Cloudification: The StreamFlow Toolkit},
author = {Iacopo Colonnelli and Barbara Cantalupo and Roberto Esposito and Matteo Pennisi and Concetto Spampinato and Marco Aldinucci},
editor = {João Bispo and Stefano Cherubin and José Flich},
url = {https://drops.dagstuhl.de/opus/volltexte/2021/13641/pdf/OASIcs-PARMA-DITAM-2021-5.pdf},
doi = {10.4230/OASIcs.PARMA-DITAM.2021.5},
issn = {2190-6807},
year = {2021},
date = {2021-01-01},
booktitle = {12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021)},
volume = {88},
pages = {5:1–5:13},
publisher = {Schloss Dagstuhl – Leibniz-Zentrum für Informatik},
address = {Dagstuhl, Germany},
series = {Open Access Series in Informatics (OASIcs)},
abstract = {Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud infrastructure can be offloaded to HPC environments to speed them up. We introduce StreamFlow, a novel Workflow Management System that supports such a design pattern and makes it possible to run the steps of a standard workflow model on independent processing elements with no shared storage. We validated the proposed approach's effectiveness on the CLAIRE COVID-19 universal pipeline, i.e. a reproducible workflow capable of automating the comparison of (possibly all) state-of-the-art pipelines for the diagnosis of COVID-19 interstitial pneumonia from CT scans images based on Deep Neural Networks (DNNs).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
D'Ascenzo, Fabrizio; Filippo, Ovidio De; Gallone, Guglielmo; Mittone, Gianluca; Deriu, Marco Agostino; Iannaccone, Mario; Ariza-Solé, Albert; Liebetrau, Christoph; Manzano-Fernández, Sergio; Quadri, Giorgio; Kinnaird, Tim; Campo, Gianluca; Henriques, Jose Paulo Simao; Hughes, James M; Dominguez-Rodriguez, Alberto; Aldinucci, Marco; Morbiducci, Umberto; Patti, Giuseppe; Raposeiras-Roubin, Sergio; Abu-Assi, Emad; Ferrari, Gaetano Maria De; Piroli, Francesco; Saglietto, Andrea; Conrotto, Federico; Omedé, Pierluigi; Montefusco, Antonio; Pennone, Mauro; Bruno, Francesco; Bocchino, Pier Paolo; Boccuzzi, Giacomo; Cerrato, Enrico; Varbella, Ferdinando; Sperti, Michela; Wilton, Stephen B.; Velicki, Lazar; Xanthopoulou, Ioanna; Cequier, Angel; Iniguez-Romo, Andres; Pousa, Isabel Munoz; Fernandez, Maria Cespon; Queija, Berenice Caneiro; Cobas-Paz, Rafael; Lopez-Cuenca, Angel; Garay, Alberto; Blanco, Pedro Flores; Rognoni, Andrea; Zoccai, Giuseppe Biondi; Biscaglia, Simone; Nunez-Gil, Ivan; Fujii, Toshiharu; Durante, Alessandro; Song, Xiantao; Kawaji, Tetsuma; Alexopoulos, Dimitrios; Huczek, Zenon; Juanatey, Jose Ramon Gonzalez; Nie, Shao-Ping; Kawashiri, Masa-aki; Colonnelli, Iacopo; Cantalupo, Barbara; Esposito, Roberto; Leonardi, Sergio; Marra, Walter Grosso; Chieffo, Alaide; Michelucci, Umberto; Piga, Dario; Malavolta, Marta; Gili, Sebastiano; Mennuni, Marco; Montalto, Claudio; Visconti, Luigi Oltrona; Arfat, Yasir
Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets Journal Article
In: The Lancet, vol. 397, no. 10270, pp. 199–207, 2021, ISSN: 0140-6736.
@article{21:lancet,
title = {Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets},
author = {Fabrizio D'Ascenzo and Ovidio De Filippo and Guglielmo Gallone and Gianluca Mittone and Marco Agostino Deriu and Mario Iannaccone and Albert Ariza-Solé and Christoph Liebetrau and Sergio Manzano-Fernández and Giorgio Quadri and Tim Kinnaird and Gianluca Campo and Jose Paulo Simao Henriques and James M Hughes and Alberto Dominguez-Rodriguez and Marco Aldinucci and Umberto Morbiducci and Giuseppe Patti and Sergio Raposeiras-Roubin and Emad Abu-Assi and Gaetano Maria De Ferrari and Francesco Piroli and Andrea Saglietto and Federico Conrotto and Pierluigi Omedé and Antonio Montefusco and Mauro Pennone and Francesco Bruno and Pier Paolo Bocchino and Giacomo Boccuzzi and Enrico Cerrato and Ferdinando Varbella and Michela Sperti and Stephen B. Wilton and Lazar Velicki and Ioanna Xanthopoulou and Angel Cequier and Andres Iniguez-Romo and Isabel Munoz Pousa and Maria Cespon Fernandez and Berenice Caneiro Queija and Rafael Cobas-Paz and Angel Lopez-Cuenca and Alberto Garay and Pedro Flores Blanco and Andrea Rognoni and Giuseppe Biondi Zoccai and Simone Biscaglia and Ivan Nunez-Gil and Toshiharu Fujii and Alessandro Durante and Xiantao Song and Tetsuma Kawaji and Dimitrios Alexopoulos and Zenon Huczek and Jose Ramon Gonzalez Juanatey and Shao-Ping Nie and Masa-aki Kawashiri and Iacopo Colonnelli and Barbara Cantalupo and Roberto Esposito and Sergio Leonardi and Walter Grosso Marra and Alaide Chieffo and Umberto Michelucci and Dario Piga and Marta Malavolta and Sebastiano Gili and Marco Mennuni and Claudio Montalto and Luigi Oltrona Visconti and Yasir Arfat},
url = {https://www.researchgate.net/profile/James_Hughes3/publication/348501148_Machine_learning-based_prediction_of_adverse_events_following_an_acute_coronary_syndrome_PRAISE_a_modelling_study_of_pooled_datasets/links/6002a81ba6fdccdcb858b6c2/Machine-learning-based-prediction-of-adverse-events-following-an-acute-coronary-syndrome-PRAISE-a-modelling-study-of-pooled-datasets.pdf},
doi = {10.1016/S0140-6736(20)32519-8},
issn = {0140-6736},
year = {2021},
date = {2021-01-01},
journal = {The Lancet},
volume = {397},
number = {10270},
pages = {199–207},
abstract = {Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding. Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aldinucci, Marco
High-performance computing and AI team up for COVID-19 diagnostic imaging Miscellaneous
AIhub, 2021, ((magazine)).
@misc{21:covid:aihub,
title = {High-performance computing and AI team up for COVID-19 diagnostic imaging},
author = {Marco Aldinucci},
url = {https://aihub.org/2021/01/12/high-performance-computing-and-ai-team-up-for-covid-19-diagnostic-imaging/},
year = {2021},
date = {2021-01-01},
abstract = {The Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) taskforce on AI & COVID-19 supported the creation of a research group focused on AI-assisted diagnosis of COVID-19 pneumonia. The first results demonstrate the great potential of AI-assisted diagnostic imaging. Furthermore, the impact of the taskforce work is much larger, and it embraces the cross-fertilisation of artificial intelligence (AI) and high-performance computing (HPC): a partnership with rocketing potential for many scientific domains.},
howpublished = {AIhub},
note = {(magazine)},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Colonnelli, Iacopo; Cantalupo, Barbara; Merelli, Ivan; Aldinucci, Marco
StreamFlow: cross-breeding cloud with HPC Journal Article
In: IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 4, pp. 1723–1737, 2021.
@article{20Lstreamflow:tetc,
title = {StreamFlow: cross-breeding cloud with HPC},
author = {Iacopo Colonnelli and Barbara Cantalupo and Ivan Merelli and Marco Aldinucci},
url = {https://arxiv.org/pdf/2002.01558},
doi = {10.1109/TETC.2020.3019202},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Emerging Topics in Computing},
volume = {9},
number = {4},
pages = {1723–1737},
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single cell transcriptomic data analysis workflow.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
Aldinucci, Marco
Polmonite da COVID-19, diagnosi con l'intelligenza artificiale: Italia in prima fila Miscellaneous
Agenda Digitale, 2020, ((magazine)).
@misc{20:covid:ag,
title = {Polmonite da COVID-19, diagnosi con l'intelligenza artificiale: Italia in prima fila},
author = {Marco Aldinucci},
url = {https://www.agendadigitale.eu/sanita/polmonite-da-covid-19-allo-studio-la-diagnosi-tramite-intelligenza-artificiale-italia-in-prima-fila/},
year = {2020},
date = {2020-11-01},
abstract = {La Task Force su AI&COVID-19 della confederazione europea dei laboratori di ricerca sull'intelligenza artificiale (CLAIRE) ha sostenuto la creazione di un gruppo di ricerca focalizzato sulla diagnosi della polmonite da COVID assistita dall'Intelligenza Artificiale. I primi risultati sono incoraggianti},
howpublished = {Agenda Digitale},
note = {(magazine)},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Turin, Gianluca; Borgarelli, Andrea; Donetti, Simone; Johnsen, Einar Broch; Tarifa, Silvia Lizeth Tapia; Damiani, Ferruccio
A formal model of the kubernetes container framework Proceedings Article
In: International Symposium on Leveraging Applications of Formal Methods, pp. 558–577, Springer 2020.
@inproceedings{turin2020formal,
title = {A formal model of the kubernetes container framework},
author = {Gianluca Turin and Andrea Borgarelli and Simone Donetti and Einar Broch Johnsen and Silvia Lizeth Tapia Tarifa and Ferruccio Damiani},
url = {https://link.springer.com/chapter/10.1007/978-3-030-61362-4_32},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {International Symposium on Leveraging Applications of Formal Methods},
pages = {558--577},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bottero, Luca; Calisto, Francesco; Graziano, Giovanni; Pagliarino, Valerio; Scauda, Martina; Tiengo, Sara; Azeglio, Simone
Physics-informed machine learning simulator for wildfire propagation Journal Article
In: arXiv preprint arXiv:2012.06825, 2020.
@article{bottero2020physicsb,
title = {Physics-informed machine learning simulator for wildfire propagation},
author = {Luca Bottero and Francesco Calisto and Giovanni Graziano and Valerio Pagliarino and Martina Scauda and Sara Tiengo and Simone Azeglio},
url = {https://arxiv.org/abs/2012.06825},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {arXiv preprint arXiv:2012.06825},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pernice, Simone; Castagno, Paolo; Marcotulli, Linda; Maule, Milena Maria; Richiardi, Lorenzo; Moirano, Giovenale; Sereno, Matteo; Cordero, Francesca; Beccuti, Marco
Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region Journal Article
In: BMC infectious diseases, vol. 20, no. 1, pp. 1–9, 2020.
@article{pernice2020impacts,
title = {Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region},
author = {Simone Pernice and Paolo Castagno and Linda Marcotulli and Milena Maria Maule and Lorenzo Richiardi and Giovenale Moirano and Matteo Sereno and Francesca Cordero and Marco Beccuti},
url = {https://link.springer.com/article/10.1186/s12879-020-05490-w},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {BMC infectious diseases},
volume = {20},
number = {1},
pages = {1--9},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pernice, Simone; Follia, Laura; Maglione, Alessandro; Pennisi, Marzio; Pappalardo, Francesco; Novelli, Francesco; Clerico, Marinella; Beccuti, Marco; Cordero, Francesca; Rolla, Simona
Computational modeling of the immune response in multiple sclerosis using epimod framework Journal Article
In: BMC bioinformatics, vol. 21, no. 17, pp. 1–20, 2020.
@article{pernice2020computational,
title = {Computational modeling of the immune response in multiple sclerosis using epimod framework},
author = {Simone Pernice and Laura Follia and Alessandro Maglione and Marzio Pennisi and Francesco Pappalardo and Francesco Novelli and Marinella Clerico and Marco Beccuti and Francesca Cordero and Simona Rolla},
url = {https://link.springer.com/article/10.1186/s12859-020-03823-9},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {BMC bioinformatics},
volume = {21},
number = {17},
pages = {1--20},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cesare, Valentina; Colonnelli, Iacopo; Aldinucci, Marco
Practical Parallelization of Scientific Applications Proceedings Article
In: Proc. of 28th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), pp. 376–384, IEEE, Västerås, Sweden, 2020.
@inproceedings{20:looppar:pdp,
title = {Practical Parallelization of Scientific Applications},
author = {Valentina Cesare and Iacopo Colonnelli and Marco Aldinucci},
url = {https://iris.unito.it/retrieve/handle/2318/1735377/601141/2020_looppar_PDP.pdf},
doi = {10.1109/PDP50117.2020.00064},
year = {2020},
date = {2020-01-01},
booktitle = {Proc. of 28th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
pages = {376–384},
publisher = {IEEE},
address = {Västerås, Sweden},
abstract = {This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a limited re-designing effort, turning an old codebase into modern code, i.e., parallel and robust code. We propose an automatable methodology to parallelize scientific applications designed with a purely sequential programming mindset, thus possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate the methodology by way of an astrophysical application, where we model at the same time the kinematic profiles of 30 disk galaxies with a Monte Carlo Markov Chain (MCMC), which is sequential by definition. The parallel code exhibits a 12 times speedup on a 48-core platform.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2019
Aldinucci, Marco; Berzovini, Claudio; Grana, Costantino; Grangetto, Marco; Pireddu, Luca; Zanetti, Gianluigi
Deep Learning e calcolo ad alte prestazioni per l'elaborazione di immagini biomediche Miscellaneous
Ital-IA: Convegno Nazionale CINI sull'Intelligenza Artificiale, 2019.
@misc{19:italia,
title = {Deep Learning e calcolo ad alte prestazioni per l'elaborazione di immagini biomediche},
author = {Marco Aldinucci and Claudio Berzovini and Costantino Grana and Marco Grangetto and Luca Pireddu and Gianluigi Zanetti},
year = {2019},
date = {2019-03-01},
abstract = {Il progetto DeepHealth, recentemente finanziato dalla Commissione Europea, ha come obiettivo la realizzazione di un ecosistema europeo costituito da piattaforme di calcolo ad alte prestazioni, librerie software e competenze multi-disciplinari di intelligenza artificiale, calcolo parallelo e scienze mediche per l'elaborazione e la diagnosi basata su immagini. Il contributo presenta sinteticamente le competenze e le infrastrutture nazionali coinvolte nel progetto.},
howpublished = {Ital-IA: Convegno Nazionale CINI sull'Intelligenza Artificiale},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
D’Agostino, Daniele; Li`o, Pietro; Aldinucci, Marco; Merelli, Ivan
NeoHiC: a web application for the analysis of Hi-C data Proceedings Article
In: International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, pp. 98–107, Springer 2019.
@inproceedings{d2019neohicb,
title = {NeoHiC: a web application for the analysis of Hi-C data},
author = {Daniele D’Agostino and Pietro Li`o and Marco Aldinucci and Ivan Merelli},
url = {https://link.springer.com/chapter/10.1007/978-3-030-63061-4_10},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics},
pages = {98--107},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Drocco, Maurizio; Viviani, Paolo; Colonnelli, Iacopo; Aldinucci, Marco; Grangetto, Marco
Accelerating spectral graph analysis through wavefronts of linear algebra operations Proceedings Article
In: 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 9–16, IEEE 2019.
@inproceedings{drocco2019accelerating,
title = {Accelerating spectral graph analysis through wavefronts of linear algebra operations},
author = {Maurizio Drocco and Paolo Viviani and Iacopo Colonnelli and Marco Aldinucci and Marco Grangetto},
url = {https://ieeexplore.ieee.org/abstract/document/8671640},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
pages = {9--16},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Viviani, Paolo
Deep Learning at Scale with Nearest Neighbours Communications Journal Article
In: 2019.
@article{viviani2019deep,
title = {Deep Learning at Scale with Nearest Neighbours Communications},
author = {Paolo Viviani},
url = {https://iris.unito.it/handle/2318/1714931},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2018
Aldinucci, Marco; Rabellino, Sergio; Pironti, Marco; Spiga, Filippo; Viviani, Paolo; Drocco, Maurizio; Guerzoni, Marco; Boella, Guido; Mellia, Marco; Margara, Paolo; Drago, Idillio; Marturano, Roberto; Marchetto, Guido; Piccolo, Elio; Bagnasco, Stefano; Lusso, Stefano; Vallero, Sara; Attardi, Giuseppe; Barchiesi, Alex; Colla, Alberto; Galeazzi, Fulvio
HPC4AI, an AI-on-demand federated platform endeavour Proceedings Article
In: ACM Computing Frontiers, Ischia, Italy, 2018.
@inproceedings{18:hpc4ai_acm_CF,
title = {HPC4AI, an AI-on-demand federated platform endeavour},
author = {Marco Aldinucci and Sergio Rabellino and Marco Pironti and Filippo Spiga and Paolo Viviani and Maurizio Drocco and Marco Guerzoni and Guido Boella and Marco Mellia and Paolo Margara and Idillio Drago and Roberto Marturano and Guido Marchetto and Elio Piccolo and Stefano Bagnasco and Stefano Lusso and Sara Vallero and Giuseppe Attardi and Alex Barchiesi and Alberto Colla and Fulvio Galeazzi},
url = {https://iris.unito.it/retrieve/handle/2318/1765596/689772/2018_hpc4ai_ACM_CF.pdf},
doi = {10.1145/3203217.3205340},
year = {2018},
date = {2018-05-01},
booktitle = {ACM Computing Frontiers},
address = {Ischia, Italy},
abstract = {In April 2018, under the auspices of the POR-FESR 2014-2020 program of Italian Piedmont Region, the Turin's Centre on High-Performance Computing for Artificial Intelligence (HPC4AI) was funded with a capital investment of 4.5Me and it began its deployment. HPC4AI aims to facilitate scientific research and engineering in the areas of Artificial Intelligence and Big Data Analytics. HPC4AI will specifically focus on methods for the on-demand provisioning of AI and BDA Cloud services to the regional and national industrial community, which includes the large regional ecosystem of Small-Medium Enterprises (SMEs) active in many different sectors such as automotive, aerospace, mechatronics, manufacturing, health and agrifood.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Aldinucci, Marco; Rabellino, Sergio; Pironti, Marco; Spiga, Filippo; Viviani, Paolo; Drocco, Maurizio; Guerzoni, Marco; Boella, Guido; Mellia, Marco; Margara, Paolo; others,
HPC4AI: an AI-on-demand federated platform endeavour Proceedings Article
In: Proceedings of the 15th ACM International Conference on Computing Frontiers, pp. 279–286, 2018.
@inproceedings{aldinucci2018hpc4ai,
title = {HPC4AI: an AI-on-demand federated platform endeavour},
author = {Marco Aldinucci and Sergio Rabellino and Marco Pironti and Filippo Spiga and Paolo Viviani and Maurizio Drocco and Marco Guerzoni and Guido Boella and Marco Mellia and Paolo Margara and others},
url = {https://iris.unito.it/bitstream/2318/1765596/1/2018_hpc4ai_ACM_CF.pdf},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Proceedings of the 15th ACM International Conference on Computing Frontiers},
pages = {279--286},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
0000
Aldinucci, Marco; Cantalupo, Barbara; Colonnelli, Iacopo; Grangetto, Marco; Renzulli, Riccardo; Tartaglione, Enzo; Grosso, Marco; Limerutti, Giorgio
Lung nodules segmentation in CT scans by DeepHealth toolkit Journal Article
In: 0000.
@article{aldinuccilung,
title = {Lung nodules segmentation in CT scans by DeepHealth toolkit},
author = {Marco Aldinucci and Barbara Cantalupo and Iacopo Colonnelli and Marco Grangetto and Riccardo Renzulli and Enzo Tartaglione and Marco Grosso and Giorgio Limerutti},
url = {https://www.micc.unifi.it/icpr2020/wp-content/uploads/demos/s1.1-paper.pdf},
keywords = {},
pubstate = {published},
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}